{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/eddy/Desktop/CPS_replication/main-analysis.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 5 Mar 2022, 16:00:43
{txt}
{com}. 
. *** Table 2 ***
. // Model 1: media freedom variable based on FOTP (w/o WGI controls)
. eststo HLM1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -68636.79}  
Iteration 1:{space 3}log pseudolikelihood = {res: -68636.79}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   436.45
{txt}Log pseudolikelihood = {res} -68636.79{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0782844{col 27}{space 2} .0319902{col 38}{space 1}   -2.45{col 47}{space 3}0.014{col 55}{space 4}-.1409841{col 68}{space 3}-.0155848
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3990899{col 27}{space 2} .0584697{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5136883{col 68}{space 3}-.2844915
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1631844{col 27}{space 2} .0545849{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0561999{col 68}{space 3}  .270169
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215541{col 27}{space 2} .0044225{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4}-.0302221{col 68}{space 3}-.0128861
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261877{col 27}{space 2} .0052115{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0159734{col 68}{space 3}  .036402
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0299267{col 27}{space 2} .0593507{col 38}{space 1}    0.50{col 47}{space 3}0.614{col 55}{space 4}-.0863986{col 68}{space 3}  .146252
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1383134{col 27}{space 2} .0860623{col 38}{space 1}   -1.61{col 47}{space 3}0.108{col 55}{space 4}-.3069924{col 68}{space 3} .0303655
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1630424{col 27}{space 2} .0309282{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1024243{col 68}{space 3} .2236605
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0103996{col 27}{space 2} .0380561{col 38}{space 1}    0.27{col 47}{space 3}0.785{col 55}{space 4}-.0641891{col 68}{space 3} .0849882
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}  .109376{col 27}{space 2} .2779578{col 38}{space 1}    0.39{col 47}{space 3}0.694{col 55}{space 4}-.4354113{col 68}{space 3} .6541633
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0417986{col 27}{space 2} .0245778{col 38}{space 1}    1.70{col 47}{space 3}0.089{col 55}{space 4} -.006373{col 68}{space 3} .0899701
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.498313{col 27}{space 2} 2.819743{col 38}{space 1}    0.89{col 47}{space 3}0.376{col 55}{space 4}-3.028281{col 68}{space 3} 8.024908
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0489332{col 44} .0289195{col 58} .0153654{col 70} .1558343
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.654979{col 44} .3814729{col 58} 1.053393{col 70} 2.600127
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315281{col 44} .4072907{col 58} 4.574059{col 70} 6.176618
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HLM1:HLM1}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68636.79{col 50}    15{col 58} 137303.6{col 69} 137428.4
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 2: media freedom variable based on FOTP (w/ WGI controls)
. eststo HLM2: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68631.426}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68631.426}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   372.26
{txt}Log pseudolikelihood = {res}-68631.426{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1061012{col 27}{space 2}  .025119{col 38}{space 1}   -4.22{col 47}{space 3}0.000{col 55}{space 4}-.1553335{col 68}{space 3}-.0568689
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3989991{col 27}{space 2} .0585217{col 38}{space 1}   -6.82{col 47}{space 3}0.000{col 55}{space 4}-.5136996{col 68}{space 3}-.2842986
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1631917{col 27}{space 2} .0545531{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0562695{col 68}{space 3} .2701139
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215587{col 27}{space 2} .0044185{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-.0302189{col 68}{space 3}-.0128985
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261895{col 27}{space 2} .0052044{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4}  .015989{col 68}{space 3} .0363899
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0299164{col 27}{space 2} .0593287{col 38}{space 1}    0.50{col 47}{space 3}0.614{col 55}{space 4}-.0863657{col 68}{space 3} .1461985
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1377753{col 27}{space 2} .0859739{col 38}{space 1}   -1.60{col 47}{space 3}0.109{col 55}{space 4}-.3062812{col 68}{space 3} .0307305
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1629687{col 27}{space 2} .0309413{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4}  .102325{col 68}{space 3} .2236125
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0105571{col 27}{space 2}  .038051{col 38}{space 1}    0.28{col 47}{space 3}0.781{col 55}{space 4}-.0640216{col 68}{space 3} .0851357
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2} -.601139{col 27}{space 2}  .254812{col 38}{space 1}   -2.36{col 47}{space 3}0.018{col 55}{space 4}-1.100561{col 68}{space 3}-.1017167
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0120131{col 27}{space 2} .0226143{col 38}{space 1}   -0.53{col 47}{space 3}0.595{col 55}{space 4}-.0563363{col 68}{space 3} .0323102
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0229466{col 27}{space 2} .0405351{col 38}{space 1}    0.57{col 47}{space 3}0.571{col 55}{space 4}-.0565008{col 68}{space 3}  .102394
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0414893{col 27}{space 2} .0324758{col 38}{space 1}    1.28{col 47}{space 3}0.201{col 55}{space 4}-.0221621{col 68}{space 3} .1051408
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.365628{col 27}{space 2} 2.089748{col 38}{space 1}    3.52{col 47}{space 3}0.000{col 55}{space 4} 3.269797{col 68}{space 3} 11.46146
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0491067{col 44} .0289987{col 58} .0154342{col 70} .1562416
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.014239{col 44} .1927567{col 58} .6988254{col 70} 1.472013
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315275{col 44} .4072834{col 58} 4.574065{col 70} 6.176595
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HLM2:HLM2}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68631.43{col 50}    17{col 58} 137296.9{col 69} 137438.3
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 3: media freedom variable based on MSF (w/o WGI controls)
. eststo HLM3: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -68634.69}  
Iteration 1:{space 3}log pseudolikelihood = {res: -68634.69}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   376.37
{txt}Log pseudolikelihood = {res} -68634.69{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0705892{col 27}{space 2} .0189436{col 38}{space 1}   -3.73{col 47}{space 3}0.000{col 55}{space 4} -.107718{col 68}{space 3}-.0334605
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3994171{col 27}{space 2} .0584865{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5140484{col 68}{space 3}-.2847857
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1630535{col 27}{space 2} .0545851{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0560686{col 68}{space 3} .2700385
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215557{col 27}{space 2} .0044249{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4}-.0302283{col 68}{space 3}-.0128831
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261847{col 27}{space 2} .0052122{col 38}{space 1}    5.02{col 47}{space 3}0.000{col 55}{space 4} .0159689{col 68}{space 3} .0364004
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0299488{col 27}{space 2} .0593388{col 38}{space 1}    0.50{col 47}{space 3}0.614{col 55}{space 4}-.0863532{col 68}{space 3} .1462508
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1385707{col 27}{space 2} .0860431{col 38}{space 1}   -1.61{col 47}{space 3}0.107{col 55}{space 4} -.307212{col 68}{space 3} .0300706
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1631282{col 27}{space 2} .0309149{col 38}{space 1}    5.28{col 47}{space 3}0.000{col 55}{space 4} .1025362{col 68}{space 3} .2237202
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0104703{col 27}{space 2}  .038061{col 38}{space 1}    0.28{col 47}{space 3}0.783{col 55}{space 4}-.0641278{col 68}{space 3} .0850684
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.2445783{col 27}{space 2} .2888522{col 38}{space 1}   -0.85{col 47}{space 3}0.397{col 55}{space 4}-.8107182{col 68}{space 3} .3215616
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0041736{col 27}{space 2} .0244335{col 38}{space 1}    0.17{col 47}{space 3}0.864{col 55}{space 4}-.0437152{col 68}{space 3} .0520624
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.366042{col 27}{space 2} 3.088846{col 38}{space 1}    2.06{col 47}{space 3}0.039{col 55}{space 4} .3120151{col 68}{space 3} 12.42007
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0492172{col 44} .0291258{col 58} .0154309{col 70} .1569793
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.366364{col 44} .2918679{col 58} .8989638{col 70} 2.076782
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315269{col 44} .4072887{col 58}  4.57405{col 70} 6.176601
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HLM3:HLM3}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68634.69{col 50}    15{col 58} 137299.4{col 69} 137424.2
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 4: media freedom variable based on MSF (w WGI controls)
. eststo HLM4: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68631.221}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68631.221}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   369.36
{txt}Log pseudolikelihood = {res}-68631.221{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0701488{col 27}{space 2} .0120468{col 38}{space 1}   -5.82{col 47}{space 3}0.000{col 55}{space 4}-.0937601{col 68}{space 3}-.0465376
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3993514{col 27}{space 2} .0585183{col 38}{space 1}   -6.82{col 47}{space 3}0.000{col 55}{space 4}-.5140452{col 68}{space 3}-.2846576
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1630784{col 27}{space 2} .0545375{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0561869{col 68}{space 3} .2699699
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215712{col 27}{space 2}  .004422{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-.0302381{col 68}{space 3}-.0129043
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261985{col 27}{space 2}  .005206{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0159949{col 68}{space 3} .0364021
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0296997{col 27}{space 2} .0593953{col 38}{space 1}    0.50{col 47}{space 3}0.617{col 55}{space 4} -.086713{col 68}{space 3} .1461124
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} -.138296{col 27}{space 2} .0859454{col 38}{space 1}   -1.61{col 47}{space 3}0.108{col 55}{space 4}-.3067458{col 68}{space 3} .0301538
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1630263{col 27}{space 2} .0309106{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1024426{col 68}{space 3}   .22361
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0104987{col 27}{space 2} .0380172{col 38}{space 1}    0.28{col 47}{space 3}0.782{col 55}{space 4}-.0640136{col 68}{space 3} .0850111
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7899527{col 27}{space 2} .2943262{col 38}{space 1}   -2.68{col 47}{space 3}0.007{col 55}{space 4}-1.366822{col 68}{space 3}-.2130839
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0459441{col 27}{space 2}  .028461{col 38}{space 1}   -1.61{col 47}{space 3}0.106{col 55}{space 4}-.1017268{col 68}{space 3} .0098385
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0292688{col 27}{space 2} .0386234{col 38}{space 1}   -0.76{col 47}{space 3}0.449{col 55}{space 4}-.1049693{col 68}{space 3} .0464318
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0682379{col 27}{space 2} .0314821{col 38}{space 1}    2.17{col 47}{space 3}0.030{col 55}{space 4} .0065342{col 68}{space 3} .1299416
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.799496{col 27}{space 2} 2.570157{col 38}{space 1}    3.81{col 47}{space 3}0.000{col 55}{space 4} 4.762081{col 68}{space 3} 14.83691
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0493597{col 44} .0292115{col 58} .0154748{col 70} .1574421
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9954221{col 44} .2940893{col 58} .5578619{col 70} 1.776183
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315263{col 44} .4072826{col 58} 4.574055{col 70} 6.176582
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HLM4:HLM4}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68631.22{col 50}    17{col 58} 137296.4{col 69} 137437.9
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Export table (Table 2 is modified from this exported table)
. esttab HLM* using "~/Desktop/CPS_replication/tables/Table 2.tex", ///
>         replace se b(4) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table 2.tex"'})

{com}. eststo clear
{txt}
{com}. 
. *** Figure 5 ***
. // Set the plot scheme (run the two following lines to replicate the asethetics of the graphs)
. * net install cleanplots, from("https://tdmize.github.io/data/cleanplots")
. * set scheme cleanplots
. 
. // Panel A
. mixed diff_dem_vdem c.internet##c.new_fotp ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65551.072}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65551.068}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,051
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       770
{txt}{col 63}avg{col 67}={col 69}{res}   1,383.4
{txt}{col 63}max{col 67}={col 69}{res}     1,942
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   511.58
{txt}Log pseudolikelihood = {res}-65551.068{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 87:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}        diff_dem_vdem{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      z{col 55}   P>|z|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}internet {c |}{col 23}{res}{space 2}-.1213567{col 35}{space 2} .0498275{col 46}{space 1}   -2.44{col 55}{space 3}0.015{col 63}{space 4}-.2190169{col 76}{space 3}-.0236966
{txt}{space 13}new_fotp {c |}{col 23}{res}{space 2}-.1081399{col 35}{space 2} .0241506{col 46}{space 1}   -4.48{col 55}{space 3}0.000{col 63}{space 4}-.1554741{col 76}{space 3}-.0608057
{txt}{space 21} {c |}
c.internet#c.new_fotp {c |}{col 23}{res}{space 2} .0020026{col 35}{space 2} .0015738{col 46}{space 1}    1.27{col 55}{space 3}0.203{col 63}{space 4}-.0010819{col 76}{space 3} .0050872
{txt}{space 21} {c |}
{space 11}university {c |}{col 23}{res}{space 2}-.3056615{col 35}{space 2} .0466412{col 46}{space 1}   -6.55{col 55}{space 3}0.000{col 63}{space 4}-.3970766{col 76}{space 3}-.2142464
{txt}{space 15}female {c |}{col 23}{res}{space 2} .1468282{col 35}{space 2} .0486948{col 46}{space 1}    3.02{col 55}{space 3}0.003{col 63}{space 4} .0513881{col 76}{space 3} .2422683
{txt}{space 18}age {c |}{col 23}{res}{space 2}-.0256727{col 35}{space 2} .0048788{col 46}{space 1}   -5.26{col 55}{space 3}0.000{col 63}{space 4} -.035235{col 76}{space 3}-.0161104
{txt}{space 15}age_sq {c |}{col 23}{res}{space 2} .0277503{col 35}{space 2} .0052731{col 46}{space 1}    5.26{col 55}{space 3}0.000{col 63}{space 4} .0174151{col 76}{space 3} .0380855
{txt}{space 14}married {c |}{col 23}{res}{space 2} .0273798{col 35}{space 2} .0613977{col 46}{space 1}    0.45{col 55}{space 3}0.656{col 63}{space 4}-.0929576{col 76}{space 3} .1477171
{txt}{space 11}unemployed {c |}{col 23}{res}{space 2}-.1548463{col 35}{space 2} .0831346{col 46}{space 1}   -1.86{col 55}{space 3}0.063{col 63}{space 4} -.317787{col 76}{space 3} .0080944
{txt}{space 15}income {c |}{col 23}{res}{space 2} .1564411{col 35}{space 2} .0283683{col 46}{space 1}    5.51{col 55}{space 3}0.000{col 63}{space 4} .1008403{col 76}{space 3} .2120419
{txt}{space 9}social_class {c |}{col 23}{res}{space 2} .0225569{col 35}{space 2} .0368705{col 46}{space 1}    0.61{col 55}{space 3}0.541{col 63}{space 4}-.0497078{col 76}{space 3} .0948217
{txt}{space 15}ln_gdp {c |}{col 23}{res}{space 2}-.6240514{col 35}{space 2}  .252995{col 46}{space 1}   -2.47{col 55}{space 3}0.014{col 63}{space 4}-1.119912{col 76}{space 3}-.1281904
{txt}{space 8}growth_one_yr {c |}{col 23}{res}{space 2}-.0080449{col 35}{space 2} .0221153{col 46}{space 1}   -0.36{col 55}{space 3}0.716{col 63}{space 4}-.0513901{col 76}{space 3} .0353003
{txt}{space 14}new_rol {c |}{col 23}{res}{space 2} .0279131{col 35}{space 2} .0403348{col 46}{space 1}    0.69{col 55}{space 3}0.489{col 63}{space 4}-.0511417{col 76}{space 3} .1069679
{txt}{space 14}new_gov {c |}{col 23}{res}{space 2} .0409472{col 35}{space 2} .0320572{col 46}{space 1}    1.28{col 55}{space 3}0.201{col 63}{space 4}-.0218839{col 76}{space 3} .1037782
{txt}{space 16}_cons {c |}{col 23}{res}{space 2}  7.73921{col 35}{space 2} 2.133204{col 46}{space 1}    3.63{col 55}{space 3}0.000{col 63}{space 4} 3.558208{col 76}{space 3} 11.92021
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0139552{col 44} .0117426{col 58} .0026822{col 70} .0726072
{txt}{space 15}var(internet) {c |}{res}{col 33} .0054289{col 44} .0032445{col 58} .0016827{col 70} .0175153
{txt}{space 18}var(_cons) {c |}{res}{col 33}  .952068{col 44} .1943444{col 58} .6381361{col 70} 1.420439
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.310216{col 44} .4187952{col 58} 4.549685{col 70} 6.197878
{txt}{hline 29}{c BT}{hline 48}

{com}. margins, at(new_fotp = (10 30 50) internet = (0 2 4)) atmeans vsquish
{res}
{txt}{col 1}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:29,051}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:0}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:10}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:2._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:0}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:30}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:3._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:0}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:50}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:4._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:2}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:10}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:5._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:2}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:30}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:6._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:2}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:50}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:7._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:4}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:10}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:8._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:4}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:30}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:9._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:4}}
{lalign 7:}{space 0}{lalign 13:new_fotp} = {res:{ralign 8:50}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 3.777367{col 26}{space 2} .5401711{col 37}{space 1}    6.99{col 46}{space 3}0.000{col 54}{space 4} 2.718651{col 67}{space 3} 4.836083
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1.614569{col 26}{space 2}  .223625{col 37}{space 1}    7.22{col 46}{space 3}0.000{col 54}{space 4} 1.176272{col 67}{space 3} 2.052866
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5482291{col 26}{space 2}  .524243{col 37}{space 1}   -1.05{col 46}{space 3}0.296{col 54}{space 4}-1.575726{col 67}{space 3} .4792683
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 3.574706{col 26}{space 2} .5710253{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} 2.455517{col 67}{space 3} 4.693895
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 1.492014{col 26}{space 2} .2254472{col 37}{space 1}    6.62{col 46}{space 3}0.000{col 54}{space 4} 1.050146{col 67}{space 3} 1.933883
{txt}{space 10}6  {c |}{col 14}{res}{space 2} -.590678{col 26}{space 2} .5420479{col 37}{space 1}   -1.09{col 46}{space 3}0.276{col 54}{space 4}-1.653072{col 67}{space 3} .4717163
{txt}{space 10}7  {c |}{col 14}{res}{space 2} 3.372046{col 26}{space 2} .6088771{col 37}{space 1}    5.54{col 46}{space 3}0.000{col 54}{space 4} 2.178669{col 67}{space 3} 4.565423
{txt}{space 10}8  {c |}{col 14}{res}{space 2} 1.369459{col 26}{space 2}  .234388{col 37}{space 1}    5.84{col 46}{space 3}0.000{col 54}{space 4} .9100674{col 67}{space 3} 1.828852
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.6331269{col 26}{space 2} .5699663{col 37}{space 1}   -1.11{col 46}{space 3}0.267{col 54}{space 4} -1.75024{col 67}{space 3} .4839865
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, noci x(new_fotp) ///
>         xtitle("Freedom of the Press") title("") ///
>         xsize(4) legend(ring(0) pos(1)) plotopts(msize(large))
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:new_fotp internet}{p_end}
{res}{txt}
{com}. graph export "~/Desktop/CPS_replication/figures/Figure 5a.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Desktop/CPS_replication/figures/Figure 5a.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. // Panel B
. mixed diff_dem_vdem c.internet##c.new_msf ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65551.046}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65551.043}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,051
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       770
{txt}{col 63}avg{col 67}={col 69}{res}   1,383.4
{txt}{col 63}max{col 67}={col 69}{res}     1,942
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   544.09
{txt}Log pseudolikelihood = {res}-65551.043{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 86:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}       diff_dem_vdem{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      z{col 54}   P>|z|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}internet {c |}{col 22}{res}{space 2}-.1389799{col 34}{space 2} .0425887{col 45}{space 1}   -3.26{col 54}{space 3}0.001{col 62}{space 4}-.2224522{col 75}{space 3}-.0555077
{txt}{space 13}new_msf {c |}{col 22}{res}{space 2}-.0695058{col 34}{space 2}  .011944{col 45}{space 1}   -5.82{col 54}{space 3}0.000{col 62}{space 4}-.0929155{col 75}{space 3} -.046096
{txt}{space 20} {c |}
c.internet#c.new_msf {c |}{col 22}{res}{space 2} .0019133{col 34}{space 2} .0008764{col 45}{space 1}    2.18{col 54}{space 3}0.029{col 62}{space 4} .0001955{col 75}{space 3}  .003631
{txt}{space 20} {c |}
{space 10}university {c |}{col 22}{res}{space 2}-.3050876{col 34}{space 2} .0464052{col 45}{space 1}   -6.57{col 54}{space 3}0.000{col 62}{space 4}-.3960402{col 75}{space 3} -.214135
{txt}{space 14}female {c |}{col 22}{res}{space 2} .1470188{col 34}{space 2} .0487645{col 45}{space 1}    3.01{col 54}{space 3}0.003{col 62}{space 4} .0514422{col 75}{space 3} .2425954
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0256546{col 34}{space 2}  .004898{col 45}{space 1}   -5.24{col 54}{space 3}0.000{col 62}{space 4}-.0352544{col 75}{space 3}-.0160548
{txt}{space 14}age_sq {c |}{col 22}{res}{space 2} .0277042{col 34}{space 2} .0052814{col 45}{space 1}    5.25{col 54}{space 3}0.000{col 62}{space 4} .0173529{col 75}{space 3} .0380554
{txt}{space 13}married {c |}{col 22}{res}{space 2} .0271164{col 34}{space 2} .0614966{col 45}{space 1}    0.44{col 54}{space 3}0.659{col 62}{space 4}-.0934147{col 75}{space 3} .1476474
{txt}{space 10}unemployed {c |}{col 22}{res}{space 2}-.1549654{col 34}{space 2} .0831653{col 45}{space 1}   -1.86{col 54}{space 3}0.062{col 62}{space 4}-.3179664{col 75}{space 3} .0080356
{txt}{space 14}income {c |}{col 22}{res}{space 2} .1564995{col 34}{space 2} .0283478{col 45}{space 1}    5.52{col 54}{space 3}0.000{col 62}{space 4} .1009388{col 75}{space 3} .2120602
{txt}{space 8}social_class {c |}{col 22}{res}{space 2} .0223527{col 34}{space 2} .0370016{col 45}{space 1}    0.60{col 54}{space 3}0.546{col 62}{space 4} -.050169{col 75}{space 3} .0948744
{txt}{space 14}ln_gdp {c |}{col 22}{res}{space 2}-.7719951{col 34}{space 2} .2883384{col 45}{space 1}   -2.68{col 54}{space 3}0.007{col 62}{space 4}-1.337128{col 75}{space 3}-.2068622
{txt}{space 7}growth_one_yr {c |}{col 22}{res}{space 2}-.0410371{col 34}{space 2} .0291857{col 45}{space 1}   -1.41{col 54}{space 3}0.160{col 62}{space 4}-.0982401{col 75}{space 3} .0161658
{txt}{space 13}new_rol {c |}{col 22}{res}{space 2}-.0274616{col 34}{space 2} .0399089{col 45}{space 1}   -0.69{col 54}{space 3}0.491{col 62}{space 4}-.1056817{col 75}{space 3} .0507584
{txt}{space 13}new_gov {c |}{col 22}{res}{space 2} .0683682{col 34}{space 2} .0317764{col 45}{space 1}    2.15{col 54}{space 3}0.031{col 62}{space 4} .0060876{col 75}{space 3} .1306488
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 9.768023{col 34}{space 2} 2.567708{col 45}{space 1}    3.80{col 54}{space 3}0.000{col 62}{space 4} 4.735408{col 75}{space 3} 14.80064
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0141999{col 44} .0116002{col 58} .0028637{col 70} .0704125
{txt}{space 15}var(internet) {c |}{res}{col 33} .0050258{col 44} .0029741{col 58} .0015758{col 70} .0160293
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.013995{col 44} .2984725{col 58} .5694847{col 70} 1.805467
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.310168{col 44} .4187981{col 58} 4.549633{col 70} 6.197837
{txt}{hline 29}{c BT}{hline 48}

{com}. margins, at(new_msf = (10 40 70) internet = (0 2 4)) atmeans vsquish
{res}
{txt}{col 1}Adjusted predictions{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:29,051}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:0}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:10}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:2._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:0}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:40}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:3._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:0}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:70}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:4._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:2}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:10}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:5._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:2}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:40}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:6._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:2}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:70}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:7._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:4}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:10}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:8._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:4}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:40}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}
{lalign 7:9._at: }{space 0}{lalign 13:internet} = {res:{ralign 8:4}}
{lalign 7:}{space 0}{lalign 13:new_msf} = {res:{ralign 8:70}}
{lalign 7:}{space 0}{lalign 13:university} = {res:{ralign 8:.1846064}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:female} = {res:{ralign 8:.5311349}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age} = {res:{ralign 8:39.98165}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:age_sq} = {res:{ralign 8:18.4005}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:married} = {res:{ralign 8:.5955389}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:unemployed} = {res:{ralign 8:.0925269}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:income} = {res:{ralign 8:3.831125}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:social_class} = {res:{ralign 8:1.660459}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:ln_gdp} = {res:{ralign 8:9.179974}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:growth_one_yr} = {res:{ralign 8:1.977292}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_rol} = {res:{ralign 8:38.22483}} {txt:(mean)}
{lalign 7:}{space 0}{lalign 13:new_gov} = {res:{ralign 8:40.36286}} {txt:(mean)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 3.759066{col 26}{space 2} .3804346{col 37}{space 1}    9.88{col 46}{space 3}0.000{col 54}{space 4} 3.013428{col 67}{space 3} 4.504704
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1.673893{col 26}{space 2} .2311082{col 37}{space 1}    7.24{col 46}{space 3}0.000{col 54}{space 4} 1.220929{col 67}{space 3} 2.126856
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.4112805{col 26}{space 2} .4678423{col 37}{space 1}   -0.88{col 46}{space 3}0.379{col 54}{space 4}-1.328234{col 67}{space 3} .5056735
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 3.519372{col 26}{space 2} .4203375{col 37}{space 1}    8.37{col 46}{space 3}0.000{col 54}{space 4} 2.695525{col 67}{space 3} 4.343218
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 1.548996{col 26}{space 2} .2387994{col 37}{space 1}    6.49{col 46}{space 3}0.000{col 54}{space 4} 1.080958{col 67}{space 3} 2.017034
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  -.42138{col 26}{space 2} .4882112{col 37}{space 1}   -0.86{col 46}{space 3}0.388{col 54}{space 4}-1.378256{col 67}{space 3} .5354963
{txt}{space 10}7  {c |}{col 14}{res}{space 2} 3.279678{col 26}{space 2} .4673622{col 37}{space 1}    7.02{col 46}{space 3}0.000{col 54}{space 4} 2.363665{col 67}{space 3} 4.195691
{txt}{space 10}8  {c |}{col 14}{res}{space 2} 1.424099{col 26}{space 2}  .252279{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4} .9296412{col 67}{space 3} 1.918557
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.4314796{col 26}{space 2} .5148835{col 37}{space 1}   -0.84{col 46}{space 3}0.402{col 54}{space 4}-1.440633{col 67}{space 3} .5776735
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, noci x(new_msf) ///
>         xtitle("Media System Freedom") title("") ///
>         xsize(4) legend(ring(0) pos(1)) plotopts(msize(large))
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:new_msf internet}{p_end}
{res}{txt}
{com}. graph export "~/Desktop/CPS_replication/figures/Figure 5b.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Desktop/CPS_replication/figures/Figure 5b.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. /* Footnote 24: interaction effects with other information sources */
. // Newspaper (p = 0.711 with FOTP; p = 0.221 with MSF)
. mixed diff_dem_vdem c.newspaper##c.new_fotp ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65409.706}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65409.704}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,991
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       759
{txt}{col 63}avg{col 67}={col 69}{res}   1,380.5
{txt}{col 63}max{col 67}={col 69}{res}     1,927
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   542.18
{txt}Log pseudolikelihood = {res}-65409.704{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 88:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}         diff_dem_vdem{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}newspaper {c |}{col 24}{res}{space 2}  .073889{col 36}{space 2} .0556213{col 47}{space 1}    1.33{col 56}{space 3}0.184{col 64}{space 4}-.0351268{col 77}{space 3} .1829047
{txt}{space 14}new_fotp {c |}{col 24}{res}{space 2}-.1042501{col 36}{space 2} .0256115{col 47}{space 1}   -4.07{col 56}{space 3}0.000{col 64}{space 4}-.1544477{col 77}{space 3}-.0540524
{txt}{space 22} {c |}
c.newspaper#c.new_fotp {c |}{col 24}{res}{space 2}-.0021233{col 36}{space 2} .0025489{col 47}{space 1}   -0.83{col 56}{space 3}0.405{col 64}{space 4} -.007119{col 77}{space 3} .0028724
{txt}{space 22} {c |}
{space 12}university {c |}{col 24}{res}{space 2}-.3150256{col 36}{space 2} .0466203{col 47}{space 1}   -6.76{col 56}{space 3}0.000{col 64}{space 4}-.4063996{col 77}{space 3}-.2236516
{txt}{space 16}female {c |}{col 24}{res}{space 2}  .150717{col 36}{space 2} .0488868{col 47}{space 1}    3.08{col 56}{space 3}0.002{col 64}{space 4} .0549007{col 77}{space 3} .2465332
{txt}{space 19}age {c |}{col 24}{res}{space 2}-.0258731{col 36}{space 2} .0049995{col 47}{space 1}   -5.18{col 56}{space 3}0.000{col 64}{space 4}-.0356719{col 77}{space 3}-.0160742
{txt}{space 16}age_sq {c |}{col 24}{res}{space 2} .0282686{col 36}{space 2} .0054788{col 47}{space 1}    5.16{col 56}{space 3}0.000{col 64}{space 4} .0175302{col 77}{space 3} .0390069
{txt}{space 15}married {c |}{col 24}{res}{space 2} .0290028{col 36}{space 2} .0607856{col 47}{space 1}    0.48{col 56}{space 3}0.633{col 64}{space 4}-.0901348{col 77}{space 3} .1481403
{txt}{space 12}unemployed {c |}{col 24}{res}{space 2} -.148524{col 36}{space 2} .0822827{col 47}{space 1}   -1.81{col 56}{space 3}0.071{col 64}{space 4}-.3097951{col 77}{space 3}  .012747
{txt}{space 16}income {c |}{col 24}{res}{space 2}  .156514{col 36}{space 2} .0284487{col 47}{space 1}    5.50{col 56}{space 3}0.000{col 64}{space 4} .1007556{col 77}{space 3} .2122723
{txt}{space 10}social_class {c |}{col 24}{res}{space 2} .0175644{col 36}{space 2} .0375497{col 47}{space 1}    0.47{col 56}{space 3}0.640{col 64}{space 4}-.0560316{col 77}{space 3} .0911604
{txt}{space 16}ln_gdp {c |}{col 24}{res}{space 2}-.6385723{col 36}{space 2} .2560175{col 47}{space 1}   -2.49{col 56}{space 3}0.013{col 64}{space 4}-1.140357{col 77}{space 3}-.1367872
{txt}{space 9}growth_one_yr {c |}{col 24}{res}{space 2}-.0097865{col 36}{space 2} .0228062{col 47}{space 1}   -0.43{col 56}{space 3}0.668{col 64}{space 4}-.0544859{col 77}{space 3} .0349128
{txt}{space 15}new_rol {c |}{col 24}{res}{space 2} .0267902{col 36}{space 2} .0405788{col 47}{space 1}    0.66{col 56}{space 3}0.509{col 64}{space 4}-.0527427{col 77}{space 3} .1063232
{txt}{space 15}new_gov {c |}{col 24}{res}{space 2} .0423224{col 36}{space 2} .0324665{col 47}{space 1}    1.30{col 56}{space 3}0.192{col 64}{space 4}-.0213107{col 77}{space 3} .1059556
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 7.719523{col 36}{space 2} 2.144441{col 47}{space 1}    3.60{col 56}{space 3}0.000{col 64}{space 4} 3.516496{col 77}{space 3} 11.92255
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0143973{col 44} .0119809{col 58}  .002818{col 70} .0735562
{txt}{space 15}var(internet) {c |}{res}{col 33} .0105319{col 44} .0055015{col 58} .0037833{col 70} .0293185
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9595493{col 44} .1950178{col 58} .6442731{col 70} 1.429107
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.30585{col 44} .4182253{col 58} 4.546324{col 70} 6.192266
{txt}{hline 29}{c BT}{hline 48}

{com}. mixed diff_dem_vdem c.newspaper##c.new_msf ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65411.311}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65411.307}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,991
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       759
{txt}{col 63}avg{col 67}={col 69}{res}   1,380.5
{txt}{col 63}max{col 67}={col 69}{res}     1,927
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   507.21
{txt}Log pseudolikelihood = {res}-65411.307{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 87:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}        diff_dem_vdem{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      z{col 55}   P>|z|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}newspaper {c |}{col 23}{res}{space 2}-.0253743{col 35}{space 2} .0631068{col 46}{space 1}   -0.40{col 55}{space 3}0.688{col 63}{space 4}-.1490614{col 76}{space 3} .0983128
{txt}{space 14}new_msf {c |}{col 23}{res}{space 2} -.070883{col 35}{space 2} .0120907{col 46}{space 1}   -5.86{col 55}{space 3}0.000{col 63}{space 4}-.0945803{col 76}{space 3}-.0471856
{txt}{space 21} {c |}
c.newspaper#c.new_msf {c |}{col 23}{res}{space 2} .0009844{col 35}{space 2}  .001319{col 46}{space 1}    0.75{col 55}{space 3}0.455{col 63}{space 4}-.0016009{col 76}{space 3} .0035697
{txt}{space 21} {c |}
{space 11}university {c |}{col 23}{res}{space 2}-.3136113{col 35}{space 2} .0467728{col 46}{space 1}   -6.70{col 55}{space 3}0.000{col 63}{space 4}-.4052844{col 76}{space 3}-.2219383
{txt}{space 15}female {c |}{col 23}{res}{space 2} .1520789{col 35}{space 2} .0489889{col 46}{space 1}    3.10{col 55}{space 3}0.002{col 63}{space 4} .0560624{col 76}{space 3} .2480955
{txt}{space 18}age {c |}{col 23}{res}{space 2}-.0256648{col 35}{space 2}  .005055{col 46}{space 1}   -5.08{col 55}{space 3}0.000{col 63}{space 4}-.0355724{col 76}{space 3}-.0157572
{txt}{space 15}age_sq {c |}{col 23}{res}{space 2} .0281143{col 35}{space 2} .0055102{col 46}{space 1}    5.10{col 55}{space 3}0.000{col 63}{space 4} .0173145{col 76}{space 3} .0389141
{txt}{space 14}married {c |}{col 23}{res}{space 2} .0295825{col 35}{space 2} .0611867{col 46}{space 1}    0.48{col 55}{space 3}0.629{col 63}{space 4}-.0903413{col 76}{space 3} .1495063
{txt}{space 11}unemployed {c |}{col 23}{res}{space 2}-.1500737{col 35}{space 2} .0821055{col 46}{space 1}   -1.83{col 55}{space 3}0.068{col 63}{space 4}-.3109975{col 76}{space 3} .0108501
{txt}{space 15}income {c |}{col 23}{res}{space 2} .1565101{col 35}{space 2} .0284301{col 46}{space 1}    5.51{col 55}{space 3}0.000{col 63}{space 4} .1007882{col 76}{space 3} .2122321
{txt}{space 9}social_class {c |}{col 23}{res}{space 2} .0180366{col 35}{space 2} .0379157{col 46}{space 1}    0.48{col 55}{space 3}0.634{col 63}{space 4}-.0562768{col 76}{space 3}   .09235
{txt}{space 15}ln_gdp {c |}{col 23}{res}{space 2}-.7735447{col 35}{space 2} .2904121{col 46}{space 1}   -2.66{col 55}{space 3}0.008{col 63}{space 4}-1.342742{col 76}{space 3}-.2043474
{txt}{space 8}growth_one_yr {c |}{col 23}{res}{space 2}-.0419752{col 35}{space 2} .0296181{col 46}{space 1}   -1.42{col 55}{space 3}0.156{col 63}{space 4}-.1000257{col 76}{space 3} .0160752
{txt}{space 14}new_rol {c |}{col 23}{res}{space 2}-.0272848{col 35}{space 2} .0401756{col 46}{space 1}   -0.68{col 55}{space 3}0.497{col 63}{space 4}-.1060276{col 76}{space 3} .0514581
{txt}{space 14}new_gov {c |}{col 23}{res}{space 2}  .067771{col 35}{space 2} .0319244{col 46}{space 1}    2.12{col 55}{space 3}0.034{col 63}{space 4} .0052003{col 76}{space 3} .1303418
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 9.818217{col 35}{space 2} 2.582074{col 46}{space 1}    3.80{col 55}{space 3}0.000{col 63}{space 4} 4.757445{col 76}{space 3} 14.87899
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0137988{col 44} .0114063{col 58} .0027304{col 70} .0697359
{txt}{space 15}var(internet) {c |}{res}{col 33} .0104377{col 44} .0054727{col 58} .0037351{col 70} .0291681
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.012952{col 44} .2988387{col 58} .5681585{col 70} 1.805961
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.306309{col 44} .4181088{col 58} 4.546974{col 70} 6.192453
{txt}{hline 29}{c BT}{hline 48}

{com}.         
. // TV (p = 0.716 with FOTP; p = 0.979 with MSF)
. mixed diff_dem_vdem c.tv##c.new_fotp ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65481.342}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65481.339}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,020
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       765
{txt}{col 63}avg{col 67}={col 69}{res}   1,381.9
{txt}{col 63}max{col 67}={col 69}{res}     1,940
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   991.84
{txt}Log pseudolikelihood = {res}-65481.339{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  diff_dem_vdem{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}tv {c |}{col 17}{res}{space 2} -.005037{col 29}{space 2} .0822385{col 40}{space 1}   -0.06{col 49}{space 3}0.951{col 57}{space 4}-.1662215{col 70}{space 3} .1561476
{txt}{space 7}new_fotp {c |}{col 17}{res}{space 2}-.1083535{col 29}{space 2} .0231463{col 40}{space 1}   -4.68{col 49}{space 3}0.000{col 57}{space 4}-.1537194{col 70}{space 3}-.0629877
{txt}{space 15} {c |}
c.tv#c.new_fotp {c |}{col 17}{res}{space 2} .0001344{col 29}{space 2} .0023667{col 40}{space 1}    0.06{col 49}{space 3}0.955{col 57}{space 4}-.0045043{col 70}{space 3} .0047731
{txt}{space 15} {c |}
{space 5}university {c |}{col 17}{res}{space 2}-.3146214{col 29}{space 2} .0472426{col 40}{space 1}   -6.66{col 49}{space 3}0.000{col 57}{space 4}-.4072152{col 70}{space 3}-.2220276
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1491745{col 29}{space 2} .0483232{col 40}{space 1}    3.09{col 49}{space 3}0.002{col 57}{space 4} .0544628{col 70}{space 3} .2438861
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0253216{col 29}{space 2} .0047582{col 40}{space 1}   -5.32{col 49}{space 3}0.000{col 57}{space 4}-.0346475{col 70}{space 3}-.0159957
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0276663{col 29}{space 2}  .005264{col 40}{space 1}    5.26{col 49}{space 3}0.000{col 57}{space 4}  .017349{col 70}{space 3} .0379836
{txt}{space 8}married {c |}{col 17}{res}{space 2}  .031078{col 29}{space 2} .0602118{col 40}{space 1}    0.52{col 49}{space 3}0.606{col 57}{space 4} -.086935{col 70}{space 3} .1490911
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.1524853{col 29}{space 2} .0836936{col 40}{space 1}   -1.82{col 49}{space 3}0.068{col 57}{space 4}-.3165217{col 70}{space 3} .0115511
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1561308{col 29}{space 2} .0282566{col 40}{space 1}    5.53{col 49}{space 3}0.000{col 57}{space 4} .1007488{col 70}{space 3} .2115128
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0201108{col 29}{space 2} .0366147{col 40}{space 1}    0.55{col 49}{space 3}0.583{col 57}{space 4}-.0516528{col 70}{space 3} .0918744
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.6242039{col 29}{space 2} .2609642{col 40}{space 1}   -2.39{col 49}{space 3}0.017{col 57}{space 4}-1.135684{col 70}{space 3}-.1127234
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0077711{col 29}{space 2} .0222223{col 40}{space 1}   -0.35{col 49}{space 3}0.727{col 57}{space 4}-.0513261{col 70}{space 3} .0357839
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2} .0281563{col 29}{space 2} .0400558{col 40}{space 1}    0.70{col 49}{space 3}0.482{col 57}{space 4}-.0503517{col 70}{space 3} .1066643
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2} .0406255{col 29}{space 2} .0319142{col 40}{space 1}    1.27{col 49}{space 3}0.203{col 57}{space 4}-.0219252{col 70}{space 3} .1031763
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 7.731093{col 29}{space 2} 2.147159{col 40}{space 1}    3.60{col 49}{space 3}0.000{col 57}{space 4} 3.522738{col 70}{space 3} 11.93945
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0134102{col 44}  .011445{col 58} .0025175{col 70} .0714328
{txt}{space 15}var(internet) {c |}{res}{col 33} .0100257{col 44} .0052535{col 58} .0035899{col 70} .0279994
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9498909{col 44} .1934826{col 58} .6372255{col 70} 1.415971
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.308426{col 44} .4190104{col 58} 4.547554{col 70} 6.196604
{txt}{hline 29}{c BT}{hline 48}

{com}. mixed diff_dem_vdem c.tv##c.new_msf ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65481.795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65481.792}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,020
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       765
{txt}{col 63}avg{col 67}={col 69}{res}   1,381.9
{txt}{col 63}max{col 67}={col 69}{res}     1,940
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   736.99
{txt}Log pseudolikelihood = {res}-65481.792{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 80:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1} diff_dem_vdem{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}tv {c |}{col 16}{res}{space 2} .0235321{col 28}{space 2} .0876901{col 39}{space 1}    0.27{col 48}{space 3}0.788{col 56}{space 4}-.1483373{col 69}{space 3} .1954016
{txt}{space 7}new_msf {c |}{col 16}{res}{space 2}-.0673424{col 28}{space 2} .0126655{col 39}{space 1}   -5.32{col 48}{space 3}0.000{col 56}{space 4}-.0921664{col 69}{space 3}-.0425185
{txt}{space 14} {c |}
c.tv#c.new_msf {c |}{col 16}{res}{space 2}-.0005926{col 28}{space 2} .0018698{col 39}{space 1}   -0.32{col 48}{space 3}0.751{col 56}{space 4}-.0042574{col 69}{space 3} .0030722
{txt}{space 14} {c |}
{space 4}university {c |}{col 16}{res}{space 2}-.3144209{col 28}{space 2} .0474342{col 39}{space 1}   -6.63{col 48}{space 3}0.000{col 56}{space 4}-.4073902{col 69}{space 3}-.2214516
{txt}{space 8}female {c |}{col 16}{res}{space 2} .1492624{col 28}{space 2} .0483957{col 39}{space 1}    3.08{col 48}{space 3}0.002{col 56}{space 4} .0544086{col 69}{space 3} .2441162
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0253756{col 28}{space 2} .0047984{col 39}{space 1}   -5.29{col 48}{space 3}0.000{col 56}{space 4}-.0347803{col 69}{space 3} -.015971
{txt}{space 8}age_sq {c |}{col 16}{res}{space 2} .0277211{col 28}{space 2}  .005291{col 39}{space 1}    5.24{col 48}{space 3}0.000{col 56}{space 4}  .017351{col 69}{space 3} .0380912
{txt}{space 7}married {c |}{col 16}{res}{space 2} .0304234{col 28}{space 2}  .059991{col 39}{space 1}    0.51{col 48}{space 3}0.612{col 56}{space 4}-.0871568{col 69}{space 3} .1480037
{txt}{space 4}unemployed {c |}{col 16}{res}{space 2}-.1526444{col 28}{space 2} .0835245{col 39}{space 1}   -1.83{col 48}{space 3}0.068{col 56}{space 4}-.3163493{col 69}{space 3} .0110606
{txt}{space 8}income {c |}{col 16}{res}{space 2}  .156161{col 28}{space 2} .0282436{col 39}{space 1}    5.53{col 48}{space 3}0.000{col 56}{space 4} .1008045{col 69}{space 3} .2115174
{txt}{space 2}social_class {c |}{col 16}{res}{space 2} .0199839{col 28}{space 2} .0366113{col 39}{space 1}    0.55{col 48}{space 3}0.585{col 56}{space 4}-.0517729{col 69}{space 3} .0917406
{txt}{space 8}ln_gdp {c |}{col 16}{res}{space 2}-.7770844{col 28}{space 2}  .294824{col 39}{space 1}   -2.64{col 48}{space 3}0.008{col 56}{space 4}-1.354929{col 69}{space 3}  -.19924
{txt}{space 1}growth_one_yr {c |}{col 16}{res}{space 2}-.0414616{col 28}{space 2} .0290535{col 39}{space 1}   -1.43{col 48}{space 3}0.154{col 56}{space 4}-.0984054{col 69}{space 3} .0154822
{txt}{space 7}new_rol {c |}{col 16}{res}{space 2}-.0285733{col 28}{space 2} .0406535{col 39}{space 1}   -0.70{col 48}{space 3}0.482{col 56}{space 4}-.1082527{col 69}{space 3} .0511061
{txt}{space 7}new_gov {c |}{col 16}{res}{space 2} .0693555{col 28}{space 2} .0321416{col 39}{space 1}    2.16{col 48}{space 3}0.031{col 56}{space 4} .0063591{col 69}{space 3}  .132352
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 9.707922{col 28}{space 2} 2.544813{col 39}{space 1}    3.81{col 48}{space 3}0.000{col 56}{space 4} 4.720181{col 69}{space 3} 14.69566
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33}  .013499{col 44} .0115245{col 58} .0025328{col 70} .0719441
{txt}{space 15}var(internet) {c |}{res}{col 33} .0100786{col 44} .0053162{col 58} .0035844{col 70}  .028339
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.012596{col 44} .3005783{col 58} .5659344{col 70} 1.811784
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.308323{col 44} .4192149{col 58} 4.547109{col 70}  6.19697
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Radio (p = 0.846 with FOTP; p = 0.343 with MSF)
. mixed diff_dem_vdem c.radio##c.new_fotp ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65365.922}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65365.915}  
Iteration 2:{space 3}log pseudolikelihood = {res:-65365.915}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,975
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       769
{txt}{col 63}avg{col 67}={col 69}{res}   1,379.8
{txt}{col 63}max{col 67}={col 69}{res}     1,926
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   938.61
{txt}Log pseudolikelihood = {res}-65365.915{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 84:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}     diff_dem_vdem{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}radio {c |}{col 20}{res}{space 2}  .018738{col 32}{space 2} .0302909{col 43}{space 1}    0.62{col 52}{space 3}0.536{col 60}{space 4}-.0406311{col 73}{space 3} .0781071
{txt}{space 10}new_fotp {c |}{col 20}{res}{space 2} -.110613{col 32}{space 2}  .023258{col 43}{space 1}   -4.76{col 52}{space 3}0.000{col 60}{space 4}-.1561978{col 73}{space 3}-.0650281
{txt}{space 18} {c |}
c.radio#c.new_fotp {c |}{col 20}{res}{space 2} .0011905{col 32}{space 2} .0012938{col 43}{space 1}    0.92{col 52}{space 3}0.358{col 60}{space 4}-.0013454{col 73}{space 3} .0037264
{txt}{space 18} {c |}
{space 8}university {c |}{col 20}{res}{space 2}-.3109495{col 32}{space 2} .0467071{col 43}{space 1}   -6.66{col 52}{space 3}0.000{col 60}{space 4}-.4024937{col 73}{space 3}-.2194053
{txt}{space 12}female {c |}{col 20}{res}{space 2}  .159577{col 32}{space 2} .0489995{col 43}{space 1}    3.26{col 52}{space 3}0.001{col 60}{space 4} .0635397{col 73}{space 3} .2556143
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0258092{col 32}{space 2} .0048366{col 43}{space 1}   -5.34{col 52}{space 3}0.000{col 60}{space 4}-.0352888{col 73}{space 3}-.0163296
{txt}{space 12}age_sq {c |}{col 20}{res}{space 2} .0277796{col 32}{space 2} .0053179{col 43}{space 1}    5.22{col 52}{space 3}0.000{col 60}{space 4} .0173567{col 73}{space 3} .0382024
{txt}{space 11}married {c |}{col 20}{res}{space 2} .0247786{col 32}{space 2} .0616398{col 43}{space 1}    0.40{col 52}{space 3}0.688{col 60}{space 4}-.0960332{col 73}{space 3} .1455903
{txt}{space 8}unemployed {c |}{col 20}{res}{space 2}-.1438693{col 32}{space 2} .0815576{col 43}{space 1}   -1.76{col 52}{space 3}0.078{col 60}{space 4}-.3037194{col 73}{space 3} .0159807
{txt}{space 12}income {c |}{col 20}{res}{space 2} .1550991{col 32}{space 2} .0283513{col 43}{space 1}    5.47{col 52}{space 3}0.000{col 60}{space 4} .0995316{col 73}{space 3} .2106667
{txt}{space 6}social_class {c |}{col 20}{res}{space 2}   .01784{col 32}{space 2} .0369301{col 43}{space 1}    0.48{col 52}{space 3}0.629{col 60}{space 4}-.0545416{col 73}{space 3} .0902216
{txt}{space 12}ln_gdp {c |}{col 20}{res}{space 2}-.6141056{col 32}{space 2} .2590706{col 43}{space 1}   -2.37{col 52}{space 3}0.018{col 60}{space 4}-1.121875{col 73}{space 3}-.1063365
{txt}{space 5}growth_one_yr {c |}{col 20}{res}{space 2}-.0087314{col 32}{space 2} .0225015{col 43}{space 1}   -0.39{col 52}{space 3}0.698{col 60}{space 4}-.0528336{col 73}{space 3} .0353708
{txt}{space 11}new_rol {c |}{col 20}{res}{space 2} .0273659{col 32}{space 2} .0418672{col 43}{space 1}    0.65{col 52}{space 3}0.513{col 60}{space 4}-.0546923{col 73}{space 3} .1094241
{txt}{space 11}new_gov {c |}{col 20}{res}{space 2} .0414387{col 32}{space 2} .0334107{col 43}{space 1}    1.24{col 52}{space 3}0.215{col 60}{space 4}-.0240452{col 73}{space 3} .1069225
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 7.612079{col 32}{space 2} 2.194045{col 43}{space 1}    3.47{col 52}{space 3}0.001{col 60}{space 4}  3.31183{col 73}{space 3} 11.91233
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0115265{col 44} .0112831{col 58} .0016923{col 70} .0785099
{txt}{space 15}var(internet) {c |}{res}{col 33} .0104631{col 44} .0052999{col 58}  .003877{col 70} .0282373
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9641275{col 44} .1959232{col 58} .6473801{col 70} 1.435852
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.303261{col 44} .4175443{col 58} 4.544907{col 70} 6.188154
{txt}{hline 29}{c BT}{hline 48}

{com}. mixed diff_dem_vdem c.radio##c.new_msf ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65364.343}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65364.335}  
Iteration 2:{space 3}log pseudolikelihood = {res:-65364.335}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,975
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       769
{txt}{col 63}avg{col 67}={col 69}{res}   1,379.8
{txt}{col 63}max{col 67}={col 69}{res}     1,926
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}  1547.44
{txt}Log pseudolikelihood = {res}-65364.335{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}    diff_dem_vdem{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}radio {c |}{col 19}{res}{space 2}-.0074237{col 31}{space 2} .0287669{col 42}{space 1}   -0.26{col 51}{space 3}0.796{col 59}{space 4}-.0638059{col 72}{space 3} .0489585
{txt}{space 10}new_msf {c |}{col 19}{res}{space 2}-.0724931{col 31}{space 2} .0114761{col 42}{space 1}   -6.32{col 51}{space 3}0.000{col 59}{space 4}-.0949858{col 72}{space 3}-.0500004
{txt}{space 17} {c |}
c.radio#c.new_msf {c |}{col 19}{res}{space 2} .0015215{col 31}{space 2} .0008695{col 42}{space 1}    1.75{col 51}{space 3}0.080{col 59}{space 4}-.0001828{col 72}{space 3} .0032257
{txt}{space 17} {c |}
{space 7}university {c |}{col 19}{res}{space 2}-.3105993{col 31}{space 2} .0464689{col 42}{space 1}   -6.68{col 51}{space 3}0.000{col 59}{space 4}-.4016768{col 72}{space 3}-.2195219
{txt}{space 11}female {c |}{col 19}{res}{space 2} .1603321{col 31}{space 2} .0491391{col 42}{space 1}    3.26{col 51}{space 3}0.001{col 59}{space 4} .0640212{col 72}{space 3}  .256643
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0257889{col 31}{space 2} .0048351{col 42}{space 1}   -5.33{col 51}{space 3}0.000{col 59}{space 4}-.0352655{col 72}{space 3}-.0163123
{txt}{space 11}age_sq {c |}{col 19}{res}{space 2} .0277863{col 31}{space 2} .0053213{col 42}{space 1}    5.22{col 51}{space 3}0.000{col 59}{space 4} .0173567{col 72}{space 3}  .038216
{txt}{space 10}married {c |}{col 19}{res}{space 2}  .024438{col 31}{space 2} .0617921{col 42}{space 1}    0.40{col 51}{space 3}0.692{col 59}{space 4}-.0966723{col 72}{space 3} .1455482
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1432521{col 31}{space 2} .0816811{col 42}{space 1}   -1.75{col 51}{space 3}0.079{col 59}{space 4}-.3033441{col 72}{space 3} .0168399
{txt}{space 11}income {c |}{col 19}{res}{space 2} .1550595{col 31}{space 2} .0282812{col 42}{space 1}    5.48{col 51}{space 3}0.000{col 59}{space 4} .0996293{col 72}{space 3} .2104896
{txt}{space 5}social_class {c |}{col 19}{res}{space 2} .0171961{col 31}{space 2} .0368353{col 42}{space 1}    0.47{col 51}{space 3}0.641{col 59}{space 4}-.0549999{col 72}{space 3}  .089392
{txt}{space 11}ln_gdp {c |}{col 19}{res}{space 2}-.7622274{col 31}{space 2} .2894239{col 42}{space 1}   -2.63{col 51}{space 3}0.008{col 59}{space 4}-1.329488{col 72}{space 3} -.194967
{txt}{space 4}growth_one_yr {c |}{col 19}{res}{space 2}-.0420643{col 31}{space 2} .0288088{col 42}{space 1}   -1.46{col 51}{space 3}0.144{col 59}{space 4}-.0985284{col 72}{space 3} .0143998
{txt}{space 10}new_rol {c |}{col 19}{res}{space 2}-.0287834{col 31}{space 2}  .041744{col 42}{space 1}   -0.69{col 51}{space 3}0.490{col 59}{space 4}-.1106001{col 72}{space 3} .0530333
{txt}{space 10}new_gov {c |}{col 19}{res}{space 2} .0695111{col 31}{space 2} .0332009{col 42}{space 1}    2.09{col 51}{space 3}0.036{col 59}{space 4} .0044386{col 72}{space 3} .1345836
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 9.691893{col 31}{space 2} 2.563728{col 42}{space 1}    3.78{col 51}{space 3}0.000{col 59}{space 4} 4.667078{col 72}{space 3} 14.71671
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0114013{col 44} .0112241{col 58} .0016557{col 70} .0785101
{txt}{space 15}var(internet) {c |}{res}{col 33} .0103721{col 44} .0053168{col 58} .0037978{col 70} .0283271
{txt}{space 18}var(_cons) {c |}{res}{col 33}  1.02713{col 44} .3042831{col 58} .5747256{col 70} 1.835653
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.30248{col 44} .4172157{col 58} 4.544685{col 70} 6.186631
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. *** Table S1 ***
. ** Generate a corresponding table for Figure 5
. // Model 1
. eststo MECH1: mixed diff_dem_vdem c.internet##c.new_fotp ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65557.082}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65557.078}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,051
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       770
{txt}{col 63}avg{col 67}={col 69}{res}   1,383.4
{txt}{col 63}max{col 67}={col 69}{res}     1,942
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   705.45
{txt}Log pseudolikelihood = {res}-65557.078{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 87:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}        diff_dem_vdem{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      z{col 55}   P>|z|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}internet {c |}{col 23}{res}{space 2} -.121155{col 35}{space 2} .0498738{col 46}{space 1}   -2.43{col 55}{space 3}0.015{col 63}{space 4}-.2189059{col 76}{space 3}-.0234041
{txt}{space 13}new_fotp {c |}{col 23}{res}{space 2}-.0786539{col 35}{space 2} .0313401{col 46}{space 1}   -2.51{col 55}{space 3}0.012{col 63}{space 4}-.1400793{col 76}{space 3}-.0172285
{txt}{space 21} {c |}
c.internet#c.new_fotp {c |}{col 23}{res}{space 2} .0019993{col 35}{space 2} .0015741{col 46}{space 1}    1.27{col 55}{space 3}0.204{col 63}{space 4}-.0010858{col 76}{space 3} .0050844
{txt}{space 21} {c |}
{space 11}university {c |}{col 23}{res}{space 2}-.3057395{col 35}{space 2} .0466252{col 46}{space 1}   -6.56{col 55}{space 3}0.000{col 63}{space 4}-.3971232{col 76}{space 3}-.2143558
{txt}{space 15}female {c |}{col 23}{res}{space 2} .1469681{col 35}{space 2} .0487394{col 46}{space 1}    3.02{col 55}{space 3}0.003{col 63}{space 4} .0514406{col 76}{space 3} .2424956
{txt}{space 18}age {c |}{col 23}{res}{space 2}-.0257147{col 35}{space 2} .0048799{col 46}{space 1}   -5.27{col 55}{space 3}0.000{col 63}{space 4}-.0352792{col 76}{space 3}-.0161503
{txt}{space 15}age_sq {c |}{col 23}{res}{space 2} .0278164{col 35}{space 2} .0052826{col 46}{space 1}    5.27{col 55}{space 3}0.000{col 63}{space 4} .0174628{col 76}{space 3}   .03817
{txt}{space 14}married {c |}{col 23}{res}{space 2} .0274592{col 35}{space 2}  .061454{col 46}{space 1}    0.45{col 55}{space 3}0.655{col 63}{space 4}-.0929883{col 76}{space 3} .1479068
{txt}{space 11}unemployed {c |}{col 23}{res}{space 2}-.1550108{col 35}{space 2} .0833332{col 46}{space 1}   -1.86{col 55}{space 3}0.063{col 63}{space 4}-.3183408{col 76}{space 3} .0083192
{txt}{space 15}income {c |}{col 23}{res}{space 2} .1565337{col 35}{space 2} .0283674{col 46}{space 1}    5.52{col 55}{space 3}0.000{col 63}{space 4} .1009347{col 76}{space 3} .2121327
{txt}{space 9}social_class {c |}{col 23}{res}{space 2} .0224071{col 35}{space 2} .0368579{col 46}{space 1}    0.61{col 55}{space 3}0.543{col 63}{space 4}-.0498331{col 76}{space 3} .0946473
{txt}{space 15}ln_gdp {c |}{col 23}{res}{space 2} .1300592{col 35}{space 2} .2716053{col 46}{space 1}    0.48{col 55}{space 3}0.632{col 63}{space 4}-.4022774{col 76}{space 3} .6623958
{txt}{space 8}growth_one_yr {c |}{col 23}{res}{space 2} .0469326{col 35}{space 2} .0241988{col 46}{space 1}    1.94{col 55}{space 3}0.052{col 63}{space 4}-.0004962{col 76}{space 3} .0943614
{txt}{space 16}_cons {c |}{col 23}{res}{space 2}  2.56441{col 35}{space 2} 2.719663{col 46}{space 1}    0.94{col 55}{space 3}0.346{col 63}{space 4}-2.766031{col 76}{space 3} 7.894851
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0138592{col 44}  .011701{col 58}  .002649{col 70} .0725087
{txt}{space 15}var(internet) {c |}{res}{col 33} .0054108{col 44} .0032471{col 58}  .001669{col 70} .0175418
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.694441{col 44} .4143741{col 58} 1.049218{col 70} 2.736447
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.310229{col 44} .4188121{col 58}  4.54967{col 70} 6.197929
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MECH1:MECH1}}{col 14}{c |}{res}{col 16}    29,051{col 28}        .{col 39}-65557.08{col 50}    18{col 58} 131150.2{col 69} 131299.1
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 2
. eststo MECH2: mixed diff_dem_vdem c.internet##c.new_fotp ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65551.072}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65551.068}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,051
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       770
{txt}{col 63}avg{col 67}={col 69}{res}   1,383.4
{txt}{col 63}max{col 67}={col 69}{res}     1,942
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   511.58
{txt}Log pseudolikelihood = {res}-65551.068{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 87:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}        diff_dem_vdem{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      z{col 55}   P>|z|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}internet {c |}{col 23}{res}{space 2}-.1213567{col 35}{space 2} .0498275{col 46}{space 1}   -2.44{col 55}{space 3}0.015{col 63}{space 4}-.2190169{col 76}{space 3}-.0236966
{txt}{space 13}new_fotp {c |}{col 23}{res}{space 2}-.1081399{col 35}{space 2} .0241506{col 46}{space 1}   -4.48{col 55}{space 3}0.000{col 63}{space 4}-.1554741{col 76}{space 3}-.0608057
{txt}{space 21} {c |}
c.internet#c.new_fotp {c |}{col 23}{res}{space 2} .0020026{col 35}{space 2} .0015738{col 46}{space 1}    1.27{col 55}{space 3}0.203{col 63}{space 4}-.0010819{col 76}{space 3} .0050872
{txt}{space 21} {c |}
{space 11}university {c |}{col 23}{res}{space 2}-.3056615{col 35}{space 2} .0466412{col 46}{space 1}   -6.55{col 55}{space 3}0.000{col 63}{space 4}-.3970766{col 76}{space 3}-.2142464
{txt}{space 15}female {c |}{col 23}{res}{space 2} .1468282{col 35}{space 2} .0486948{col 46}{space 1}    3.02{col 55}{space 3}0.003{col 63}{space 4} .0513881{col 76}{space 3} .2422683
{txt}{space 18}age {c |}{col 23}{res}{space 2}-.0256727{col 35}{space 2} .0048788{col 46}{space 1}   -5.26{col 55}{space 3}0.000{col 63}{space 4} -.035235{col 76}{space 3}-.0161104
{txt}{space 15}age_sq {c |}{col 23}{res}{space 2} .0277503{col 35}{space 2} .0052731{col 46}{space 1}    5.26{col 55}{space 3}0.000{col 63}{space 4} .0174151{col 76}{space 3} .0380855
{txt}{space 14}married {c |}{col 23}{res}{space 2} .0273798{col 35}{space 2} .0613977{col 46}{space 1}    0.45{col 55}{space 3}0.656{col 63}{space 4}-.0929576{col 76}{space 3} .1477171
{txt}{space 11}unemployed {c |}{col 23}{res}{space 2}-.1548463{col 35}{space 2} .0831346{col 46}{space 1}   -1.86{col 55}{space 3}0.063{col 63}{space 4} -.317787{col 76}{space 3} .0080944
{txt}{space 15}income {c |}{col 23}{res}{space 2} .1564411{col 35}{space 2} .0283683{col 46}{space 1}    5.51{col 55}{space 3}0.000{col 63}{space 4} .1008403{col 76}{space 3} .2120419
{txt}{space 9}social_class {c |}{col 23}{res}{space 2} .0225569{col 35}{space 2} .0368705{col 46}{space 1}    0.61{col 55}{space 3}0.541{col 63}{space 4}-.0497078{col 76}{space 3} .0948217
{txt}{space 15}ln_gdp {c |}{col 23}{res}{space 2}-.6240514{col 35}{space 2}  .252995{col 46}{space 1}   -2.47{col 55}{space 3}0.014{col 63}{space 4}-1.119912{col 76}{space 3}-.1281904
{txt}{space 8}growth_one_yr {c |}{col 23}{res}{space 2}-.0080449{col 35}{space 2} .0221153{col 46}{space 1}   -0.36{col 55}{space 3}0.716{col 63}{space 4}-.0513901{col 76}{space 3} .0353003
{txt}{space 14}new_rol {c |}{col 23}{res}{space 2} .0279131{col 35}{space 2} .0403348{col 46}{space 1}    0.69{col 55}{space 3}0.489{col 63}{space 4}-.0511417{col 76}{space 3} .1069679
{txt}{space 14}new_gov {c |}{col 23}{res}{space 2} .0409472{col 35}{space 2} .0320572{col 46}{space 1}    1.28{col 55}{space 3}0.201{col 63}{space 4}-.0218839{col 76}{space 3} .1037782
{txt}{space 16}_cons {c |}{col 23}{res}{space 2}  7.73921{col 35}{space 2} 2.133204{col 46}{space 1}    3.63{col 55}{space 3}0.000{col 63}{space 4} 3.558208{col 76}{space 3} 11.92021
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0139552{col 44} .0117426{col 58} .0026822{col 70} .0726072
{txt}{space 15}var(internet) {c |}{res}{col 33} .0054289{col 44} .0032445{col 58} .0016827{col 70} .0175153
{txt}{space 18}var(_cons) {c |}{res}{col 33}  .952068{col 44} .1943444{col 58} .6381361{col 70} 1.420439
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.310216{col 44} .4187952{col 58} 4.549685{col 70} 6.197878
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MECH2:MECH2}}{col 14}{c |}{res}{col 16}    29,051{col 28}        .{col 39}-65551.07{col 50}    20{col 58} 131142.1{col 69} 131307.7
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 3
. eststo MECH3: mixed diff_dem_vdem c.internet##c.new_msf ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65554.568}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65554.565}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,051
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       770
{txt}{col 63}avg{col 67}={col 69}{res}   1,383.4
{txt}{col 63}max{col 67}={col 69}{res}     1,942
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   481.74
{txt}Log pseudolikelihood = {res}-65554.565{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 86:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}       diff_dem_vdem{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      z{col 54}   P>|z|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}internet {c |}{col 22}{res}{space 2}-.1387318{col 34}{space 2} .0426228{col 45}{space 1}   -3.25{col 54}{space 3}0.001{col 62}{space 4} -.222271{col 75}{space 3}-.0551927
{txt}{space 13}new_msf {c |}{col 22}{res}{space 2}-.0711236{col 34}{space 2} .0191371{col 45}{space 1}   -3.72{col 54}{space 3}0.000{col 62}{space 4}-.1086316{col 75}{space 3}-.0336157
{txt}{space 20} {c |}
c.internet#c.new_msf {c |}{col 22}{res}{space 2} .0019095{col 34}{space 2}  .000877{col 45}{space 1}    2.18{col 54}{space 3}0.029{col 62}{space 4} .0001905{col 75}{space 3} .0036284
{txt}{space 20} {c |}
{space 10}university {c |}{col 22}{res}{space 2} -.305282{col 34}{space 2} .0464098{col 45}{space 1}   -6.58{col 54}{space 3}0.000{col 62}{space 4}-.3962435{col 75}{space 3}-.2143205
{txt}{space 14}female {c |}{col 22}{res}{space 2} .1470501{col 34}{space 2} .0488203{col 45}{space 1}    3.01{col 54}{space 3}0.003{col 62}{space 4} .0513642{col 75}{space 3} .2427361
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0256635{col 34}{space 2} .0049008{col 45}{space 1}   -5.24{col 54}{space 3}0.000{col 62}{space 4}-.0352689{col 75}{space 3}-.0160581
{txt}{space 14}age_sq {c |}{col 22}{res}{space 2} .0277239{col 34}{space 2} .0052899{col 45}{space 1}    5.24{col 54}{space 3}0.000{col 62}{space 4} .0173558{col 75}{space 3}  .038092
{txt}{space 13}married {c |}{col 22}{res}{space 2} .0274839{col 34}{space 2} .0614581{col 45}{space 1}    0.45{col 54}{space 3}0.655{col 62}{space 4}-.0929718{col 75}{space 3} .1479396
{txt}{space 10}unemployed {c |}{col 22}{res}{space 2}-.1550407{col 34}{space 2} .0833052{col 45}{space 1}   -1.86{col 54}{space 3}0.063{col 62}{space 4}-.3183159{col 75}{space 3} .0082345
{txt}{space 14}income {c |}{col 22}{res}{space 2} .1566197{col 34}{space 2} .0283588{col 45}{space 1}    5.52{col 54}{space 3}0.000{col 62}{space 4} .1010375{col 75}{space 3} .2122018
{txt}{space 8}social_class {c |}{col 22}{res}{space 2} .0223395{col 34}{space 2} .0370309{col 45}{space 1}    0.60{col 54}{space 3}0.546{col 62}{space 4}-.0502397{col 75}{space 3} .0949186
{txt}{space 14}ln_gdp {c |}{col 22}{res}{space 2}-.2142158{col 34}{space 2} .2866393{col 45}{space 1}   -0.75{col 54}{space 3}0.455{col 62}{space 4}-.7760185{col 75}{space 3}  .347587
{txt}{space 7}growth_one_yr {c |}{col 22}{res}{space 2} .0084663{col 34}{space 2} .0257445{col 45}{space 1}    0.33{col 54}{space 3}0.742{col 62}{space 4} -.041992{col 75}{space 3} .0589246
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 6.326524{col 34}{space 2} 3.066679{col 45}{space 1}    2.06{col 54}{space 3}0.039{col 62}{space 4} .3159439{col 75}{space 3}  12.3371
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0141516{col 44} .0115838{col 58} .0028448{col 70} .0703971
{txt}{space 15}var(internet) {c |}{res}{col 33} .0050273{col 44} .0029678{col 58} .0015807{col 70} .0159893
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.421432{col 44} .3106601{col 58} .9261736{col 70} 2.181524
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.31017{col 44} .4188093{col 58} 4.549616{col 70} 6.197864
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MECH3:MECH3}}{col 14}{c |}{res}{col 16}    29,051{col 28}        .{col 39}-65554.56{col 50}    18{col 58} 131145.1{col 69} 131294.1
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 4
. eststo MECH4: mixed diff_dem_vdem c.internet##c.new_msf ///
>         university female age age_sq ///
>         married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university internet, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-65551.046}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65551.043}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    29,051
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       770
{txt}{col 63}avg{col 67}={col 69}{res}   1,383.4
{txt}{col 63}max{col 67}={col 69}{res}     1,942
{col 49}{txt}Wald chi2({res}15{txt}){col 67}={col 70}{res}   544.09
{txt}Log pseudolikelihood = {res}-65551.043{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 86:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}       diff_dem_vdem{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      z{col 54}   P>|z|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}internet {c |}{col 22}{res}{space 2}-.1389799{col 34}{space 2} .0425887{col 45}{space 1}   -3.26{col 54}{space 3}0.001{col 62}{space 4}-.2224522{col 75}{space 3}-.0555077
{txt}{space 13}new_msf {c |}{col 22}{res}{space 2}-.0695058{col 34}{space 2}  .011944{col 45}{space 1}   -5.82{col 54}{space 3}0.000{col 62}{space 4}-.0929155{col 75}{space 3} -.046096
{txt}{space 20} {c |}
c.internet#c.new_msf {c |}{col 22}{res}{space 2} .0019133{col 34}{space 2} .0008764{col 45}{space 1}    2.18{col 54}{space 3}0.029{col 62}{space 4} .0001955{col 75}{space 3}  .003631
{txt}{space 20} {c |}
{space 10}university {c |}{col 22}{res}{space 2}-.3050876{col 34}{space 2} .0464052{col 45}{space 1}   -6.57{col 54}{space 3}0.000{col 62}{space 4}-.3960402{col 75}{space 3} -.214135
{txt}{space 14}female {c |}{col 22}{res}{space 2} .1470188{col 34}{space 2} .0487645{col 45}{space 1}    3.01{col 54}{space 3}0.003{col 62}{space 4} .0514422{col 75}{space 3} .2425954
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0256546{col 34}{space 2}  .004898{col 45}{space 1}   -5.24{col 54}{space 3}0.000{col 62}{space 4}-.0352544{col 75}{space 3}-.0160548
{txt}{space 14}age_sq {c |}{col 22}{res}{space 2} .0277042{col 34}{space 2} .0052814{col 45}{space 1}    5.25{col 54}{space 3}0.000{col 62}{space 4} .0173529{col 75}{space 3} .0380554
{txt}{space 13}married {c |}{col 22}{res}{space 2} .0271164{col 34}{space 2} .0614966{col 45}{space 1}    0.44{col 54}{space 3}0.659{col 62}{space 4}-.0934147{col 75}{space 3} .1476474
{txt}{space 10}unemployed {c |}{col 22}{res}{space 2}-.1549654{col 34}{space 2} .0831653{col 45}{space 1}   -1.86{col 54}{space 3}0.062{col 62}{space 4}-.3179664{col 75}{space 3} .0080356
{txt}{space 14}income {c |}{col 22}{res}{space 2} .1564995{col 34}{space 2} .0283478{col 45}{space 1}    5.52{col 54}{space 3}0.000{col 62}{space 4} .1009388{col 75}{space 3} .2120602
{txt}{space 8}social_class {c |}{col 22}{res}{space 2} .0223527{col 34}{space 2} .0370016{col 45}{space 1}    0.60{col 54}{space 3}0.546{col 62}{space 4} -.050169{col 75}{space 3} .0948744
{txt}{space 14}ln_gdp {c |}{col 22}{res}{space 2}-.7719951{col 34}{space 2} .2883384{col 45}{space 1}   -2.68{col 54}{space 3}0.007{col 62}{space 4}-1.337128{col 75}{space 3}-.2068622
{txt}{space 7}growth_one_yr {c |}{col 22}{res}{space 2}-.0410371{col 34}{space 2} .0291857{col 45}{space 1}   -1.41{col 54}{space 3}0.160{col 62}{space 4}-.0982401{col 75}{space 3} .0161658
{txt}{space 13}new_rol {c |}{col 22}{res}{space 2}-.0274616{col 34}{space 2} .0399089{col 45}{space 1}   -0.69{col 54}{space 3}0.491{col 62}{space 4}-.1056817{col 75}{space 3} .0507584
{txt}{space 13}new_gov {c |}{col 22}{res}{space 2} .0683682{col 34}{space 2} .0317764{col 45}{space 1}    2.15{col 54}{space 3}0.031{col 62}{space 4} .0060876{col 75}{space 3} .1306488
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 9.768023{col 34}{space 2} 2.567708{col 45}{space 1}    3.80{col 54}{space 3}0.000{col 62}{space 4} 4.735408{col 75}{space 3} 14.80064
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0141999{col 44} .0116002{col 58} .0028637{col 70} .0704125
{txt}{space 15}var(internet) {c |}{res}{col 33} .0050258{col 44} .0029741{col 58} .0015758{col 70} .0160293
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.013995{col 44} .2984725{col 58} .5694847{col 70} 1.805467
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.310168{col 44} .4187981{col 58} 4.549633{col 70} 6.197837
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MECH4:MECH4}}{col 14}{c |}{res}{col 16}    29,051{col 28}        .{col 39}-65551.04{col 50}    20{col 58} 131142.1{col 69} 131307.6
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Export table (Table S1 is modified from this exported table)
. esttab MECH* using "~/Desktop/CPS_replication/tables/Table S1.tex", ///
>         replace se b(4) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table S1.tex"'})

{com}. eststo clear
{txt}
{com}. 
. *** Table S2 ***
. ** Use the DV that is based on standardized Polyarchy and per_dem
. // Model 1
. eststo STD1: mixed diff_dem_vdem_z new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-36862.275}  
Iteration 1:{space 3}log pseudolikelihood = {res:-36862.275}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   442.02
{txt}Log pseudolikelihood = {res}-36862.275{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_vdem_z{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}new_fotp {c |}{col 17}{res}{space 2}-.0316863{col 29}{space 2} .0122437{col 40}{space 1}   -2.59{col 49}{space 3}0.010{col 57}{space 4}-.0556835{col 70}{space 3}-.0076891
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.1404067{col 29}{space 2} .0205612{col 40}{space 1}   -6.83{col 49}{space 3}0.000{col 57}{space 4} -.180706{col 70}{space 3}-.1001074
{txt}{space 9}female {c |}{col 17}{res}{space 2} .0574017{col 29}{space 2} .0192031{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0197643{col 70}{space 3} .0950391
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0075775{col 29}{space 2} .0015559{col 40}{space 1}   -4.87{col 49}{space 3}0.000{col 57}{space 4}-.0106271{col 70}{space 3}-.0045279
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0092064{col 29}{space 2} .0018338{col 40}{space 1}    5.02{col 49}{space 3}0.000{col 57}{space 4} .0056122{col 70}{space 3} .0128005
{txt}{space 8}married {c |}{col 17}{res}{space 2}  .010459{col 29}{space 2} .0208845{col 40}{space 1}    0.50{col 49}{space 3}0.617{col 57}{space 4}-.0304739{col 70}{space 3} .0513919
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0486662{col 29}{space 2} .0302741{col 40}{space 1}   -1.61{col 49}{space 3}0.108{col 57}{space 4}-.1080023{col 70}{space 3} .0106699
{txt}{space 9}income {c |}{col 17}{res}{space 2} .0573374{col 29}{space 2}   .01088{col 40}{space 1}    5.27{col 49}{space 3}0.000{col 57}{space 4}  .036013{col 70}{space 3} .0786617
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0036288{col 29}{space 2}  .013389{col 40}{space 1}    0.27{col 49}{space 3}0.786{col 57}{space 4}-.0226132{col 70}{space 3} .0298708
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2} .0691498{col 29}{space 2} .1048499{col 40}{space 1}    0.66{col 49}{space 3}0.510{col 57}{space 4}-.1363523{col 70}{space 3} .2746518
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2} .0087728{col 29}{space 2} .0094569{col 40}{space 1}    0.93{col 49}{space 3}0.354{col 57}{space 4}-.0097624{col 70}{space 3}  .027308
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.022403{col 29}{space 2} 1.111512{col 40}{space 1}    0.92{col 49}{space 3}0.358{col 57}{space 4}-1.156122{col 70}{space 3} 3.200927
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0060487{col 44} .0035791{col 58} .0018967{col 70}   .01929
{txt}{space 18}var(_cons) {c |}{res}{col 33}  .224137{col 44}   .04794{col 58} .1473848{col 70} .3408587
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .6577403{col 44} .0504001{col 58} .5660179{col 70} .7643263
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay STD1:STD1}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-36862.27{col 50}    15{col 58} 73754.55{col 69} 73879.39
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 2
. eststo STD2: mixed diff_dem_vdem_z new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-36856.876}  
Iteration 1:{space 3}log pseudolikelihood = {res:-36856.876}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   346.78
{txt}Log pseudolikelihood = {res}-36856.876{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_vdem_z{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}new_fotp {c |}{col 17}{res}{space 2}-.0428469{col 29}{space 2}  .010188{col 40}{space 1}   -4.21{col 49}{space 3}0.000{col 57}{space 4}-.0628149{col 70}{space 3}-.0228788
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.1403945{col 29}{space 2} .0205751{col 40}{space 1}   -6.82{col 49}{space 3}0.000{col 57}{space 4} -.180721{col 70}{space 3} -.100068
{txt}{space 9}female {c |}{col 17}{res}{space 2} .0573953{col 29}{space 2} .0191951{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0197736{col 70}{space 3}  .095017
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0075749{col 29}{space 2} .0015547{col 40}{space 1}   -4.87{col 49}{space 3}0.000{col 57}{space 4}-.0106221{col 70}{space 3}-.0045277
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0092019{col 29}{space 2} .0018318{col 40}{space 1}    5.02{col 49}{space 3}0.000{col 57}{space 4} .0056117{col 70}{space 3}  .012792
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0104423{col 29}{space 2} .0208777{col 40}{space 1}    0.50{col 49}{space 3}0.617{col 57}{space 4}-.0304774{col 70}{space 3} .0513619
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0484936{col 29}{space 2} .0302403{col 40}{space 1}   -1.60{col 49}{space 3}0.109{col 57}{space 4}-.1077634{col 70}{space 3} .0107763
{txt}{space 9}income {c |}{col 17}{res}{space 2} .0573111{col 29}{space 2}  .010884{col 40}{space 1}    5.27{col 49}{space 3}0.000{col 57}{space 4} .0359788{col 70}{space 3} .0786434
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0036748{col 29}{space 2} .0133887{col 40}{space 1}    0.27{col 49}{space 3}0.784{col 57}{space 4}-.0225665{col 70}{space 3} .0299161
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.1846264{col 29}{space 2} .0949421{col 40}{space 1}   -1.94{col 49}{space 3}0.052{col 57}{space 4}-.3707094{col 70}{space 3} .0014566
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0094403{col 29}{space 2} .0083081{col 40}{space 1}   -1.14{col 49}{space 3}0.256{col 57}{space 4}-.0257239{col 70}{space 3} .0068433
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2} .0129887{col 29}{space 2} .0142386{col 40}{space 1}    0.91{col 49}{space 3}0.362{col 57}{space 4}-.0149184{col 70}{space 3} .0408957
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2} .0112823{col 29}{space 2} .0122688{col 40}{space 1}    0.92{col 49}{space 3}0.358{col 57}{space 4}-.0127641{col 70}{space 3} .0353287
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  2.75437{col 29}{space 2} .8338568{col 40}{space 1}    3.30{col 49}{space 3}0.001{col 57}{space 4}  1.12004{col 70}{space 3} 4.388699
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0060645{col 44} .0035878{col 58}  .001902{col 70} .0193362
{txt}{space 18}var(_cons) {c |}{res}{col 33} .1369558{col 44} .0261165{col 58} .0942459{col 70} .1990209
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .6577397{col 44} .0503992{col 58} .5660187{col 70} .7643236
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay STD2:STD2}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-36856.88{col 50}    17{col 58} 73747.75{col 69} 73889.24
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 3
. eststo STD3: mixed diff_dem_vdem_z new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-36859.545}  
Iteration 1:{space 3}log pseudolikelihood = {res:-36859.545}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   360.14
{txt}Log pseudolikelihood = {res}-36859.545{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_vdem_z{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}new_msf {c |}{col 17}{res}{space 2}-.0287966{col 29}{space 2} .0075458{col 40}{space 1}   -3.82{col 49}{space 3}0.000{col 57}{space 4}-.0435861{col 70}{space 3}-.0140071
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.1405463{col 29}{space 2} .0205671{col 40}{space 1}   -6.83{col 49}{space 3}0.000{col 57}{space 4}-.1808572{col 70}{space 3}-.1002355
{txt}{space 9}female {c |}{col 17}{res}{space 2}  .057347{col 29}{space 2} .0192028{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0197103{col 70}{space 3} .0949837
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0075761{col 29}{space 2} .0015569{col 40}{space 1}   -4.87{col 49}{space 3}0.000{col 57}{space 4}-.0106275{col 70}{space 3}-.0045246
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0092025{col 29}{space 2} .0018342{col 40}{space 1}    5.02{col 49}{space 3}0.000{col 57}{space 4} .0056075{col 70}{space 3} .0127974
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0104561{col 29}{space 2} .0208836{col 40}{space 1}    0.50{col 49}{space 3}0.617{col 57}{space 4} -.030475{col 70}{space 3} .0513872
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0487752{col 29}{space 2} .0302675{col 40}{space 1}   -1.61{col 49}{space 3}0.107{col 57}{space 4}-.1080983{col 70}{space 3} .0105479
{txt}{space 9}income {c |}{col 17}{res}{space 2} .0573715{col 29}{space 2} .0108752{col 40}{space 1}    5.28{col 49}{space 3}0.000{col 57}{space 4} .0360564{col 70}{space 3} .0786865
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0036443{col 29}{space 2} .0133904{col 40}{space 1}    0.27{col 49}{space 3}0.786{col 57}{space 4}-.0226005{col 70}{space 3}  .029889
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.0757103{col 29}{space 2} .1086232{col 40}{space 1}   -0.70{col 49}{space 3}0.486{col 57}{space 4}-.2886079{col 70}{space 3} .1371873
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2} -.006638{col 29}{space 2} .0096746{col 40}{space 1}   -0.69{col 49}{space 3}0.493{col 57}{space 4}-.0255998{col 70}{space 3} .0123238
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.611915{col 29}{space 2} 1.208025{col 40}{space 1}    2.16{col 49}{space 3}0.031{col 57}{space 4} .2442294{col 70}{space 3}   4.9796
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0060847{col 44} .0036067{col 58} .0019041{col 70}  .019444
{txt}{space 18}var(_cons) {c |}{res}{col 33} .1747261{col 44} .0303561{col 58} .1243008{col 70} .2456075
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .6577388{col 44} .0503998{col 58} .5660168{col 70} .7643241
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay STD3:STD3}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-36859.55{col 50}    15{col 58} 73749.09{col 69} 73873.93
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 4
. eststo STD4: mixed diff_dem_vdem_z new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-36856.329}  
Iteration 1:{space 3}log pseudolikelihood = {res:-36856.329}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   346.19
{txt}Log pseudolikelihood = {res}-36856.329{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_vdem_z{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}new_msf {c |}{col 17}{res}{space 2}-.0287147{col 29}{space 2} .0052358{col 40}{space 1}   -5.48{col 49}{space 3}0.000{col 57}{space 4}-.0389767{col 70}{space 3}-.0184526
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.1405369{col 29}{space 2} .0205744{col 40}{space 1}   -6.83{col 49}{space 3}0.000{col 57}{space 4} -.180862{col 70}{space 3}-.1002118
{txt}{space 9}female {c |}{col 17}{res}{space 2} .0573493{col 29}{space 2} .0191891{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0197393{col 70}{space 3} .0949593
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0075788{col 29}{space 2}  .001556{col 40}{space 1}   -4.87{col 49}{space 3}0.000{col 57}{space 4}-.0106285{col 70}{space 3}-.0045292
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0092041{col 29}{space 2} .0018323{col 40}{space 1}    5.02{col 49}{space 3}0.000{col 57}{space 4} .0056129{col 70}{space 3} .0127953
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0103586{col 29}{space 2} .0209037{col 40}{space 1}    0.50{col 49}{space 3}0.620{col 57}{space 4}-.0306119{col 70}{space 3} .0513292
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0486918{col 29}{space 2} .0302306{col 40}{space 1}   -1.61{col 49}{space 3}0.107{col 57}{space 4}-.1079428{col 70}{space 3} .0105592
{txt}{space 9}income {c |}{col 17}{res}{space 2} .0573348{col 29}{space 2}  .010873{col 40}{space 1}    5.27{col 49}{space 3}0.000{col 57}{space 4} .0360242{col 70}{space 3} .0786454
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0036497{col 29}{space 2} .0133762{col 40}{space 1}    0.27{col 49}{space 3}0.785{col 57}{space 4}-.0225672{col 70}{space 3} .0298665
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.2635368{col 29}{space 2}  .112463{col 40}{space 1}   -2.34{col 49}{space 3}0.019{col 57}{space 4}-.4839603{col 70}{space 3}-.0431133
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0234324{col 29}{space 2} .0110036{col 40}{space 1}   -2.13{col 49}{space 3}0.033{col 57}{space 4} -.044999{col 70}{space 3}-.0018658
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2}-.0080294{col 29}{space 2} .0127405{col 40}{space 1}   -0.63{col 49}{space 3}0.529{col 57}{space 4}-.0330003{col 70}{space 3} .0169415
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2} .0220299{col 29}{space 2} .0112206{col 40}{space 1}    1.96{col 49}{space 3}0.050{col 57}{space 4}  .000038{col 70}{space 3} .0440217
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 3.777313{col 29}{space 2} 1.046403{col 40}{space 1}    3.61{col 49}{space 3}0.000{col 57}{space 4} 1.726401{col 70}{space 3} 5.828225
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0060992{col 44} .0036167{col 58} .0019078{col 70}  .019499
{txt}{space 18}var(_cons) {c |}{res}{col 33}  .130268{col 44}  .033061{col 58} .0792154{col 70}  .214223
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .6577382{col 44} .0503991{col 58} .5660175{col 70} .7643218
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay STD4:STD4}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-36856.33{col 50}    17{col 58} 73746.66{col 69} 73888.14
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Export table (Table S2 is modified from this exported table)
. esttab STD* using "~/Desktop/CPS_replication/tables/Table S2.tex", ///
>         replace se b(4) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table S2.tex"'})

{com}. eststo clear
{txt}
{com}. 
. *** Table S3 ***
. ** Use the Polyarchy index that excludes the media freedom component
. // Model 1
. eststo EXC1: mixed diff_dem_new_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68639.784}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68639.784}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   505.65
{txt}Log pseudolikelihood = {res}-68639.784{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}diff_dem_new_vdem{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}new_fotp {c |}{col 19}{res}{space 2}-.0890166{col 31}{space 2} .0354034{col 42}{space 1}   -2.51{col 51}{space 3}0.012{col 59}{space 4}-.1584059{col 72}{space 3}-.0196273
{txt}{space 7}university {c |}{col 19}{res}{space 2}-.3988691{col 31}{space 2} .0584355{col 42}{space 1}   -6.83{col 51}{space 3}0.000{col 59}{space 4}-.5134006{col 72}{space 3}-.2843376
{txt}{space 11}female {c |}{col 19}{res}{space 2} .1631619{col 31}{space 2}  .054613{col 42}{space 1}    2.99{col 51}{space 3}0.003{col 59}{space 4} .0561225{col 72}{space 3} .2702013
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0215183{col 31}{space 2} .0044222{col 42}{space 1}   -4.87{col 51}{space 3}0.000{col 59}{space 4}-.0301857{col 72}{space 3} -.012851
{txt}{space 11}age_sq {c |}{col 19}{res}{space 2} .0261545{col 31}{space 2} .0052144{col 42}{space 1}    5.02{col 51}{space 3}0.000{col 59}{space 4} .0159346{col 72}{space 3} .0363745
{txt}{space 10}married {c |}{col 19}{res}{space 2}  .029699{col 31}{space 2} .0593317{col 42}{space 1}    0.50{col 51}{space 3}0.617{col 59}{space 4}-.0865889{col 72}{space 3}  .145987
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1386108{col 31}{space 2} .0860338{col 42}{space 1}   -1.61{col 51}{space 3}0.107{col 59}{space 4} -.307234{col 72}{space 3} .0300124
{txt}{space 11}income {c |}{col 19}{res}{space 2} .1628574{col 31}{space 2} .0309383{col 42}{space 1}    5.26{col 51}{space 3}0.000{col 59}{space 4} .1022195{col 72}{space 3} .2234953
{txt}{space 5}social_class {c |}{col 19}{res}{space 2} .0105205{col 31}{space 2} .0381196{col 42}{space 1}    0.28{col 51}{space 3}0.783{col 59}{space 4}-.0641925{col 72}{space 3} .0852335
{txt}{space 11}ln_gdp {c |}{col 19}{res}{space 2} -.037373{col 31}{space 2} .3413713{col 42}{space 1}   -0.11{col 51}{space 3}0.913{col 59}{space 4}-.7064484{col 72}{space 3} .6317025
{txt}{space 4}growth_one_yr {c |}{col 19}{res}{space 2}-.0068929{col 31}{space 2}  .029516{col 42}{space 1}   -0.23{col 51}{space 3}0.815{col 59}{space 4}-.0647431{col 72}{space 3} .0509574
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 4.165765{col 31}{space 2} 3.463385{col 42}{space 1}    1.20{col 51}{space 3}0.229{col 59}{space 4}-2.622345{col 72}{space 3} 10.95388
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0485335{col 44}  .028852{col 58} .0151364{col 70}  .155618
{txt}{space 18}var(_cons) {c |}{res}{col 33} 2.174169{col 44} .4779581{col 58} 1.413089{col 70} 3.345161
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315299{col 44}  .407292{col 58} 4.574075{col 70} 6.176639
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay EXC1:EXC1}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68639.78{col 50}    15{col 58} 137309.6{col 69} 137434.4
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 2
. eststo EXC2: mixed diff_dem_new_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68637.175}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68637.175}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   380.65
{txt}Log pseudolikelihood = {res}-68637.175{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}diff_dem_new_vdem{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}new_fotp {c |}{col 19}{res}{space 2}-.1121755{col 31}{space 2} .0315403{col 42}{space 1}   -3.56{col 51}{space 3}0.000{col 59}{space 4}-.1739934{col 72}{space 3}-.0503577
{txt}{space 7}university {c |}{col 19}{res}{space 2}-.3987491{col 31}{space 2} .0584326{col 42}{space 1}   -6.82{col 51}{space 3}0.000{col 59}{space 4} -.513275{col 72}{space 3}-.2842233
{txt}{space 11}female {c |}{col 19}{res}{space 2} .1631686{col 31}{space 2} .0546005{col 42}{space 1}    2.99{col 51}{space 3}0.003{col 59}{space 4} .0561536{col 72}{space 3} .2701836
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0215135{col 31}{space 2} .0044201{col 42}{space 1}   -4.87{col 51}{space 3}0.000{col 59}{space 4}-.0301767{col 72}{space 3}-.0128504
{txt}{space 11}age_sq {c |}{col 19}{res}{space 2} .0261499{col 31}{space 2}  .005211{col 42}{space 1}    5.02{col 51}{space 3}0.000{col 59}{space 4} .0159365{col 72}{space 3} .0363634
{txt}{space 10}married {c |}{col 19}{res}{space 2} .0295999{col 31}{space 2} .0593151{col 42}{space 1}    0.50{col 51}{space 3}0.618{col 59}{space 4}-.0866556{col 72}{space 3} .1458555
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1384063{col 31}{space 2} .0859655{col 42}{space 1}   -1.61{col 51}{space 3}0.107{col 59}{space 4}-.3068955{col 72}{space 3}  .030083
{txt}{space 11}income {c |}{col 19}{res}{space 2} .1627542{col 31}{space 2} .0309595{col 42}{space 1}    5.26{col 51}{space 3}0.000{col 59}{space 4} .1020747{col 72}{space 3} .2234336
{txt}{space 5}social_class {c |}{col 19}{res}{space 2}  .010639{col 31}{space 2} .0381065{col 42}{space 1}    0.28{col 51}{space 3}0.780{col 59}{space 4}-.0640484{col 72}{space 3} .0853264
{txt}{space 11}ln_gdp {c |}{col 19}{res}{space 2}-.6428075{col 31}{space 2} .3218785{col 42}{space 1}   -2.00{col 51}{space 3}0.046{col 59}{space 4}-1.273678{col 72}{space 3}-.0119373
{txt}{space 4}growth_one_yr {c |}{col 19}{res}{space 2} -.053209{col 31}{space 2} .0289383{col 42}{space 1}   -1.84{col 51}{space 3}0.066{col 59}{space 4}-.1099271{col 72}{space 3} .0035091
{txt}{space 10}new_rol {c |}{col 19}{res}{space 2} .0174018{col 31}{space 2} .0565656{col 42}{space 1}    0.31{col 51}{space 3}0.758{col 59}{space 4}-.0934647{col 72}{space 3} .1282683
{txt}{space 10}new_gov {c |}{col 19}{res}{space 2} .0369479{col 31}{space 2} .0466781{col 42}{space 1}    0.79{col 51}{space 3}0.429{col 59}{space 4}-.0545396{col 72}{space 3} .1284354
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 8.316045{col 31}{space 2}  3.06757{col 42}{space 1}    2.71{col 51}{space 3}0.007{col 59}{space 4} 2.303719{col 72}{space 3} 14.32837
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33}  .048531{col 44} .0288644{col 58} .0151272{col 70} .1556976
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.714121{col 44} .4830823{col 58} .9866235{col 70} 2.978046
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315299{col 44} .4072902{col 58} 4.574078{col 70} 6.176635
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay EXC2:EXC2}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68637.18{col 50}    17{col 58} 137308.4{col 69} 137449.8
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 3
. eststo EXC3: mixed diff_dem_new_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68638.743}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68638.743}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   396.78
{txt}Log pseudolikelihood = {res}-68638.743{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}diff_dem_new_vdem{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}new_msf {c |}{col 19}{res}{space 2}-.0732795{col 31}{space 2} .0213424{col 42}{space 1}   -3.43{col 51}{space 3}0.001{col 59}{space 4}-.1151098{col 72}{space 3}-.0314492
{txt}{space 7}university {c |}{col 19}{res}{space 2}-.3990475{col 31}{space 2}  .058443{col 42}{space 1}   -6.83{col 51}{space 3}0.000{col 59}{space 4}-.5135937{col 72}{space 3}-.2845013
{txt}{space 11}female {c |}{col 19}{res}{space 2} .1630714{col 31}{space 2} .0546165{col 42}{space 1}    2.99{col 51}{space 3}0.003{col 59}{space 4} .0560251{col 72}{space 3} .2701177
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0215188{col 31}{space 2} .0044242{col 42}{space 1}   -4.86{col 51}{space 3}0.000{col 59}{space 4}  -.03019{col 72}{space 3}-.0128475
{txt}{space 11}age_sq {c |}{col 19}{res}{space 2} .0261528{col 31}{space 2} .0052158{col 42}{space 1}    5.01{col 51}{space 3}0.000{col 59}{space 4} .0159301{col 72}{space 3} .0363756
{txt}{space 10}married {c |}{col 19}{res}{space 2} .0296852{col 31}{space 2} .0593205{col 42}{space 1}    0.50{col 51}{space 3}0.617{col 59}{space 4}-.0865808{col 72}{space 3} .1459511
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1388169{col 31}{space 2} .0860128{col 42}{space 1}   -1.61{col 51}{space 3}0.107{col 59}{space 4}-.3073988{col 72}{space 3} .0297651
{txt}{space 11}income {c |}{col 19}{res}{space 2} .1628861{col 31}{space 2} .0309293{col 42}{space 1}    5.27{col 51}{space 3}0.000{col 59}{space 4} .1022657{col 72}{space 3} .2235065
{txt}{space 5}social_class {c |}{col 19}{res}{space 2} .0106064{col 31}{space 2} .0381198{col 42}{space 1}    0.28{col 51}{space 3}0.781{col 59}{space 4}-.0641071{col 72}{space 3} .0853198
{txt}{space 11}ln_gdp {c |}{col 19}{res}{space 2}-.3903908{col 31}{space 2} .3877735{col 42}{space 1}   -1.01{col 51}{space 3}0.314{col 59}{space 4}-1.150413{col 72}{space 3} .3696313
{txt}{space 4}growth_one_yr {c |}{col 19}{res}{space 2}-.0440209{col 31}{space 2} .0294641{col 42}{space 1}   -1.49{col 51}{space 3}0.135{col 59}{space 4}-.1017696{col 72}{space 3} .0137277
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 7.818405{col 31}{space 2}  3.96344{col 42}{space 1}    1.97{col 51}{space 3}0.049{col 59}{space 4} .0502051{col 72}{space 3}  15.5866
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0486841{col 44} .0289651{col 58}  .015169{col 70} .1562496
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.977382{col 44} .3804222{col 58} 1.356222{col 70} 2.883039
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315293{col 44} .4072912{col 58}  4.57407{col 70}  6.17663
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay EXC3:EXC3}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68638.74{col 50}    15{col 58} 137307.5{col 69} 137432.3
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 4
. eststo EXC4: mixed diff_dem_new_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68637.303}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68637.303}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   377.25
{txt}Log pseudolikelihood = {res}-68637.303{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}diff_dem_new_vdem{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}new_msf {c |}{col 19}{res}{space 2}-.0724402{col 31}{space 2} .0158543{col 42}{space 1}   -4.57{col 51}{space 3}0.000{col 59}{space 4}-.1035141{col 72}{space 3}-.0413663
{txt}{space 7}university {c |}{col 19}{res}{space 2} -.398951{col 31}{space 2} .0584252{col 42}{space 1}   -6.83{col 51}{space 3}0.000{col 59}{space 4}-.5134623{col 72}{space 3}-.2844396
{txt}{space 11}female {c |}{col 19}{res}{space 2} .1631049{col 31}{space 2} .0545918{col 42}{space 1}    2.99{col 51}{space 3}0.003{col 59}{space 4} .0561069{col 72}{space 3}  .270103
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0215225{col 31}{space 2} .0044221{col 42}{space 1}   -4.87{col 51}{space 3}0.000{col 59}{space 4}-.0301897{col 72}{space 3}-.0128554
{txt}{space 11}age_sq {c |}{col 19}{res}{space 2} .0261573{col 31}{space 2} .0052121{col 42}{space 1}    5.02{col 51}{space 3}0.000{col 59}{space 4} .0159418{col 72}{space 3} .0363727
{txt}{space 10}married {c |}{col 19}{res}{space 2} .0294663{col 31}{space 2} .0593549{col 42}{space 1}    0.50{col 51}{space 3}0.620{col 59}{space 4}-.0868671{col 72}{space 3} .1457997
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1387197{col 31}{space 2} .0859479{col 42}{space 1}   -1.61{col 51}{space 3}0.107{col 59}{space 4}-.3071745{col 72}{space 3} .0297351
{txt}{space 11}income {c |}{col 19}{res}{space 2}  .162786{col 31}{space 2} .0309427{col 42}{space 1}    5.26{col 51}{space 3}0.000{col 59}{space 4} .1021394{col 72}{space 3} .2234326
{txt}{space 5}social_class {c |}{col 19}{res}{space 2} .0106042{col 31}{space 2} .0380871{col 42}{space 1}    0.28{col 51}{space 3}0.781{col 59}{space 4}-.0640451{col 72}{space 3} .0852535
{txt}{space 11}ln_gdp {c |}{col 19}{res}{space 2}-.8306069{col 31}{space 2} .3816578{col 42}{space 1}   -2.18{col 51}{space 3}0.030{col 59}{space 4}-1.578643{col 72}{space 3}-.0825714
{txt}{space 4}growth_one_yr {c |}{col 19}{res}{space 2}-.0877748{col 31}{space 2} .0340837{col 42}{space 1}   -2.58{col 51}{space 3}0.010{col 59}{space 4}-.1545777{col 72}{space 3}-.0209719
{txt}{space 10}new_rol {c |}{col 19}{res}{space 2}  -.03809{col 31}{space 2}  .052036{col 42}{space 1}   -0.73{col 51}{space 3}0.464{col 59}{space 4}-.1400787{col 72}{space 3} .0638987
{txt}{space 10}new_gov {c |}{col 19}{res}{space 2} .0654592{col 31}{space 2} .0444909{col 42}{space 1}    1.47{col 51}{space 3}0.141{col 59}{space 4}-.0217413{col 72}{space 3} .1526598
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 10.71032{col 31}{space 2} 3.703096{col 42}{space 1}    2.89{col 51}{space 3}0.004{col 59}{space 4}  3.45239{col 72}{space 3} 17.96826
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0486784{col 44}  .028952{col 58} .0151731{col 70} .1561702
{txt}{space 18}var(_cons) {c |}{res}{col 33}  1.73421{col 44} .4554499{col 58} 1.036462{col 70} 2.901683
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315293{col 44} .4072894{col 58} 4.574073{col 70} 6.176627
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay EXC4:EXC4}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39} -68637.3{col 50}    17{col 58} 137308.6{col 69} 137450.1
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Export table (Table S3 is modified from this exported table)
. esttab EXC* using "~/Desktop/CPS_replication/tables/Table S3.tex", ///
>         replace se b(4) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table S3.tex"'})

{com}. eststo clear
{txt}
{com}. 
. *** Table S8 ***
. // Generate a variable for individual confidence in courts
. gen court_conf = .
{txt}(31,742 missing values generated)

{com}. replace court_conf = 0 if V114 == 4
{txt}(5,038 real changes made)

{com}. replace court_conf = 1 if V114 == 3
{txt}(9,334 real changes made)

{com}. replace court_conf = 2 if V114 == 2
{txt}(11,096 real changes made)

{com}. replace court_conf = 3 if V114 == 1
{txt}(5,456 real changes made)

{com}. 
. // Generate a variable for individual confidence in government
. gen govt_conf = .
{txt}(31,742 missing values generated)

{com}. replace govt_conf = 0 if V115 == 4
{txt}(5,826 real changes made)

{com}. replace govt_conf = 1 if V115 == 3
{txt}(9,356 real changes made)

{com}. replace govt_conf = 2 if V115 == 2
{txt}(10,705 real changes made)

{com}. replace govt_conf = 3 if V115 == 1
{txt}(5,161 real changes made)

{com}. 
. // Generate a variable for individual confidence in civil service
. gen civil_conf = .
{txt}(31,742 missing values generated)

{com}. replace civil_conf = 0 if V118 == 4
{txt}(5,481 real changes made)

{com}. replace civil_conf = 1 if V118 == 3
{txt}(9,514 real changes made)

{com}. replace civil_conf = 2 if V118 == 2
{txt}(11,928 real changes made)

{com}. replace civil_conf = 3 if V118 == 1
{txt}(3,744 real changes made)

{com}. 
. // Run the main analyses but control for confidence in courts, government, and civil service
. // Model 1
. eststo CON1: mixed diff_dem_vdem new_fotp university ///
>         court_conf govt_conf civil_conf ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64217.166}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64217.165}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,867
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       693
{txt}{col 63}avg{col 67}={col 69}{res}   1,312.1
{txt}{col 63}max{col 67}={col 69}{res}     1,915
{col 49}{txt}Wald chi2({res}14{txt}){col 67}={col 70}{res}  1522.84
{txt}Log pseudolikelihood = {res}-64217.165{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0669339{col 27}{space 2} .0279089{col 38}{space 1}   -2.40{col 47}{space 3}0.016{col 55}{space 4}-.1216344{col 68}{space 3}-.0122335
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3206831{col 27}{space 2} .0465906{col 38}{space 1}   -6.88{col 47}{space 3}0.000{col 55}{space 4} -.411999{col 68}{space 3}-.2293672
{txt}{space 3}court_conf {c |}{col 15}{res}{space 2} .1847993{col 27}{space 2} .0459376{col 38}{space 1}    4.02{col 47}{space 3}0.000{col 55}{space 4} .0947633{col 68}{space 3} .2748353
{txt}{space 4}govt_conf {c |}{col 15}{res}{space 2} .4563304{col 27}{space 2} .0446163{col 38}{space 1}   10.23{col 47}{space 3}0.000{col 55}{space 4}  .368884{col 68}{space 3} .5437768
{txt}{space 3}civil_conf {c |}{col 15}{res}{space 2} .0950871{col 27}{space 2} .0410433{col 38}{space 1}    2.32{col 47}{space 3}0.021{col 55}{space 4} .0146437{col 68}{space 3} .1755305
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1112776{col 27}{space 2} .0429165{col 38}{space 1}    2.59{col 47}{space 3}0.010{col 55}{space 4} .0271629{col 68}{space 3} .1953923
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0136228{col 27}{space 2} .0042194{col 38}{space 1}   -3.23{col 47}{space 3}0.001{col 55}{space 4}-.0218925{col 68}{space 3} -.005353
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0153448{col 27}{space 2} .0050416{col 38}{space 1}    3.04{col 47}{space 3}0.002{col 55}{space 4} .0054635{col 68}{space 3} .0252262
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0199948{col 27}{space 2} .0525074{col 38}{space 1}   -0.38{col 47}{space 3}0.703{col 55}{space 4}-.1229075{col 68}{space 3} .0829178
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1145885{col 27}{space 2} .0736833{col 38}{space 1}   -1.56{col 47}{space 3}0.120{col 55}{space 4} -.259005{col 68}{space 3}  .029828
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1527881{col 27}{space 2}  .027176{col 38}{space 1}    5.62{col 47}{space 3}0.000{col 55}{space 4}  .099524{col 68}{space 3} .2060521
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0041294{col 27}{space 2} .0355064{col 38}{space 1}   -0.12{col 47}{space 3}0.907{col 55}{space 4}-.0737205{col 68}{space 3} .0654618
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}  .000315{col 27}{space 2} .2399327{col 38}{space 1}    0.00{col 47}{space 3}0.999{col 55}{space 4}-.4699444{col 68}{space 3} .4705744
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0344853{col 27}{space 2} .0215003{col 38}{space 1}    1.60{col 47}{space 3}0.109{col 55}{space 4}-.0076544{col 68}{space 3}  .076625
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.061064{col 27}{space 2} 2.419195{col 38}{space 1}    0.85{col 47}{space 3}0.394{col 55}{space 4}-2.680471{col 68}{space 3}   6.8026
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33}  .016495{col 44} .0159684{col 58} .0024736{col 70} .1099969
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.266572{col 44} .2988856{col 58} .7975597{col 70}  2.01139
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.985615{col 44} .3947828{col 58} 4.268909{col 70} 5.822649
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay CON1:CON1}}{col 14}{c |}{res}{col 16}    28,867{col 28}        .{col 39}-64217.16{col 50}    18{col 58} 128470.3{col 69} 128619.2
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 2
. eststo CON2: mixed diff_dem_vdem new_fotp university ///
>         court_conf govt_conf civil_conf ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64212.429}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64212.428}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,867
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       693
{txt}{col 63}avg{col 67}={col 69}{res}   1,312.1
{txt}{col 63}max{col 67}={col 69}{res}     1,915
{col 49}{txt}Wald chi2({res}16{txt}){col 67}={col 70}{res}  1219.29
{txt}Log pseudolikelihood = {res}-64212.428{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0914787{col 27}{space 2} .0234853{col 38}{space 1}   -3.90{col 47}{space 3}0.000{col 55}{space 4} -.137509{col 68}{space 3}-.0454483
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3206356{col 27}{space 2} .0466148{col 38}{space 1}   -6.88{col 47}{space 3}0.000{col 55}{space 4}-.4119989{col 68}{space 3}-.2292723
{txt}{space 3}court_conf {c |}{col 15}{res}{space 2} .1846853{col 27}{space 2} .0458961{col 38}{space 1}    4.02{col 47}{space 3}0.000{col 55}{space 4} .0947305{col 68}{space 3} .2746401
{txt}{space 4}govt_conf {c |}{col 15}{res}{space 2} .4562574{col 27}{space 2} .0445592{col 38}{space 1}   10.24{col 47}{space 3}0.000{col 55}{space 4}  .368923{col 68}{space 3} .5435917
{txt}{space 3}civil_conf {c |}{col 15}{res}{space 2} .0949015{col 27}{space 2} .0410765{col 38}{space 1}    2.31{col 47}{space 3}0.021{col 55}{space 4}  .014393{col 68}{space 3} .1754099
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1112951{col 27}{space 2} .0428754{col 38}{space 1}    2.60{col 47}{space 3}0.009{col 55}{space 4}  .027261{col 68}{space 3} .1953293
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0136372{col 27}{space 2} .0042152{col 38}{space 1}   -3.24{col 47}{space 3}0.001{col 55}{space 4}-.0218989{col 68}{space 3}-.0053755
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0153595{col 27}{space 2} .0050341{col 38}{space 1}    3.05{col 47}{space 3}0.002{col 55}{space 4} .0054927{col 68}{space 3} .0252262
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0199407{col 27}{space 2} .0525052{col 38}{space 1}   -0.38{col 47}{space 3}0.704{col 55}{space 4}-.1228491{col 68}{space 3} .0829677
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1140792{col 27}{space 2} .0735658{col 38}{space 1}   -1.55{col 47}{space 3}0.121{col 55}{space 4}-.2582655{col 68}{space 3} .0301071
{txt}{space 7}income {c |}{col 15}{res}{space 2}  .152711{col 27}{space 2} .0271957{col 38}{space 1}    5.62{col 47}{space 3}0.000{col 55}{space 4} .0994083{col 68}{space 3} .2060137
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} -.003921{col 27}{space 2} .0355215{col 38}{space 1}   -0.11{col 47}{space 3}0.912{col 55}{space 4}-.0735419{col 68}{space 3} .0656999
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5798378{col 27}{space 2} .2341269{col 38}{space 1}   -2.48{col 47}{space 3}0.013{col 55}{space 4}-1.038718{col 68}{space 3}-.1209574
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0079533{col 27}{space 2} .0206655{col 38}{space 1}   -0.38{col 47}{space 3}0.700{col 55}{space 4}-.0484569{col 68}{space 3} .0325503
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0258547{col 27}{space 2} .0384068{col 38}{space 1}    0.67{col 47}{space 3}0.501{col 55}{space 4}-.0494212{col 68}{space 3} .1011306
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0286208{col 27}{space 2}  .030444{col 38}{space 1}    0.94{col 47}{space 3}0.347{col 55}{space 4}-.0310485{col 68}{space 3}   .08829
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.026839{col 27}{space 2} 1.884869{col 38}{space 1}    3.20{col 47}{space 3}0.001{col 55}{space 4} 2.332564{col 68}{space 3} 9.721115
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0166018{col 44} .0160946{col 58} .0024829{col 70} .1110078
{txt}{space 18}var(_cons) {c |}{res}{col 33}  .821741{col 44}  .166823{col 58}   .55199{col 70} 1.223316
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.985607{col 44} .3947633{col 58} 4.268934{col 70} 5.822597
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay CON2:CON2}}{col 14}{c |}{res}{col 16}    28,867{col 28}        .{col 39}-64212.43{col 50}    20{col 58} 128464.9{col 69} 128630.3
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 3
. eststo CON3: mixed diff_dem_vdem new_msf university ///
>         court_conf govt_conf civil_conf ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64215.298}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64215.297}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,867
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       693
{txt}{col 63}avg{col 67}={col 69}{res}   1,312.1
{txt}{col 63}max{col 67}={col 69}{res}     1,915
{col 49}{txt}Wald chi2({res}14{txt}){col 67}={col 70}{res}  1499.52
{txt}Log pseudolikelihood = {res}-64215.297{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0597345{col 27}{space 2} .0168921{col 38}{space 1}   -3.54{col 47}{space 3}0.000{col 55}{space 4}-.0928425{col 68}{space 3}-.0266265
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3210299{col 27}{space 2} .0465897{col 38}{space 1}   -6.89{col 47}{space 3}0.000{col 55}{space 4}-.4123439{col 68}{space 3}-.2297158
{txt}{space 3}court_conf {c |}{col 15}{res}{space 2} .1848856{col 27}{space 2} .0459445{col 38}{space 1}    4.02{col 47}{space 3}0.000{col 55}{space 4}  .094836{col 68}{space 3} .2749353
{txt}{space 4}govt_conf {c |}{col 15}{res}{space 2} .4560814{col 27}{space 2} .0445908{col 38}{space 1}   10.23{col 47}{space 3}0.000{col 55}{space 4} .3686851{col 68}{space 3} .5434778
{txt}{space 3}civil_conf {c |}{col 15}{res}{space 2} .0952343{col 27}{space 2} .0410161{col 38}{space 1}    2.32{col 47}{space 3}0.020{col 55}{space 4} .0148441{col 68}{space 3} .1756244
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1111523{col 27}{space 2} .0429303{col 38}{space 1}    2.59{col 47}{space 3}0.010{col 55}{space 4} .0270105{col 68}{space 3} .1952941
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0136287{col 27}{space 2} .0042221{col 38}{space 1}   -3.23{col 47}{space 3}0.001{col 55}{space 4} -.021904{col 68}{space 3}-.0053535
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0153466{col 27}{space 2} .0050431{col 38}{space 1}    3.04{col 47}{space 3}0.002{col 55}{space 4} .0054622{col 68}{space 3} .0252309
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0199672{col 27}{space 2} .0525039{col 38}{space 1}   -0.38{col 47}{space 3}0.704{col 55}{space 4}-.1228729{col 68}{space 3} .0829384
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1148744{col 27}{space 2} .0736534{col 38}{space 1}   -1.56{col 47}{space 3}0.119{col 55}{space 4}-.2592324{col 68}{space 3} .0294836
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1528726{col 27}{space 2} .0271605{col 38}{space 1}    5.63{col 47}{space 3}0.000{col 55}{space 4}  .099639{col 68}{space 3} .2061063
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0040587{col 27}{space 2} .0355319{col 38}{space 1}   -0.11{col 47}{space 3}0.909{col 55}{space 4}   -.0737{col 68}{space 3} .0655826
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.2979766{col 27}{space 2} .2527235{col 38}{space 1}   -1.18{col 47}{space 3}0.238{col 55}{space 4}-.7933056{col 68}{space 3} .1973523
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0028185{col 27}{space 2} .0212766{col 38}{space 1}    0.13{col 47}{space 3}0.895{col 55}{space 4}-.0388829{col 68}{space 3} .0445199
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 5.302366{col 27}{space 2} 2.688478{col 38}{space 1}    1.97{col 47}{space 3}0.049{col 55}{space 4} .0330457{col 68}{space 3} 10.57169
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0166564{col 44} .0160772{col 58} .0025118{col 70} .1104539
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.067961{col 44}  .240993{col 58} .6862396{col 70} 1.662016
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.985603{col 44} .3947876{col 58} 4.268889{col 70} 5.822648
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay CON3:CON3}}{col 14}{c |}{res}{col 16}    28,867{col 28}        .{col 39} -64215.3{col 50}    18{col 58} 128466.6{col 69} 128615.5
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 4
. eststo CON4: mixed diff_dem_vdem new_msf university ///
>         court_conf govt_conf civil_conf ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64212.493}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64212.492}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,867
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       693
{txt}{col 63}avg{col 67}={col 69}{res}   1,312.1
{txt}{col 63}max{col 67}={col 69}{res}     1,915
{col 49}{txt}Wald chi2({res}16{txt}){col 67}={col 70}{res}  1338.40
{txt}Log pseudolikelihood = {res}-64212.492{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0595261{col 27}{space 2} .0112834{col 38}{space 1}   -5.28{col 47}{space 3}0.000{col 55}{space 4}-.0816411{col 68}{space 3}-.0374111
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3209186{col 27}{space 2} .0466075{col 38}{space 1}   -6.89{col 47}{space 3}0.000{col 55}{space 4}-.4122676{col 68}{space 3}-.2295696
{txt}{space 3}court_conf {c |}{col 15}{res}{space 2} .1847017{col 27}{space 2} .0458997{col 38}{space 1}    4.02{col 47}{space 3}0.000{col 55}{space 4} .0947399{col 68}{space 3} .2746634
{txt}{space 4}govt_conf {c |}{col 15}{res}{space 2} .4560496{col 27}{space 2} .0444912{col 38}{space 1}   10.25{col 47}{space 3}0.000{col 55}{space 4} .3688485{col 68}{space 3} .5432508
{txt}{space 3}civil_conf {c |}{col 15}{res}{space 2} .0950692{col 27}{space 2} .0410697{col 38}{space 1}    2.31{col 47}{space 3}0.021{col 55}{space 4} .0145742{col 68}{space 3} .1755643
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1111984{col 27}{space 2} .0428702{col 38}{space 1}    2.59{col 47}{space 3}0.009{col 55}{space 4} .0271744{col 68}{space 3} .1952224
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0136502{col 27}{space 2} .0042179{col 38}{space 1}   -3.24{col 47}{space 3}0.001{col 55}{space 4}-.0219171{col 68}{space 3}-.0053833
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2}  .015369{col 27}{space 2} .0050356{col 38}{space 1}    3.05{col 47}{space 3}0.002{col 55}{space 4} .0054995{col 68}{space 3} .0252385
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0201632{col 27}{space 2} .0525788{col 38}{space 1}   -0.38{col 47}{space 3}0.701{col 55}{space 4}-.1232158{col 68}{space 3} .0828893
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1146084{col 27}{space 2} .0735303{col 38}{space 1}   -1.56{col 47}{space 3}0.119{col 55}{space 4}-.2587253{col 68}{space 3} .0295084
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1527644{col 27}{space 2} .0271661{col 38}{space 1}    5.62{col 47}{space 3}0.000{col 55}{space 4} .0995198{col 68}{space 3}  .206009
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0039911{col 27}{space 2} .0355074{col 38}{space 1}   -0.11{col 47}{space 3}0.911{col 55}{space 4}-.0735842{col 68}{space 3} .0656021
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7360385{col 27}{space 2} .2713205{col 38}{space 1}   -2.71{col 47}{space 3}0.007{col 55}{space 4}-1.267817{col 68}{space 3}-.2042601
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0364865{col 27}{space 2} .0261673{col 38}{space 1}   -1.39{col 47}{space 3}0.163{col 55}{space 4}-.0877735{col 68}{space 3} .0148005
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0193343{col 27}{space 2} .0364001{col 38}{space 1}   -0.53{col 47}{space 3}0.595{col 55}{space 4}-.0906772{col 68}{space 3} .0520085
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0518179{col 27}{space 2} .0299545{col 38}{space 1}    1.73{col 47}{space 3}0.084{col 55}{space 4}-.0068918{col 68}{space 3} .1105276
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 8.026239{col 27}{space 2} 2.334005{col 38}{space 1}    3.44{col 47}{space 3}0.001{col 55}{space 4} 3.451673{col 68}{space 3} 12.60081
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0166998{col 44} .0161388{col 58} .0025125{col 70} .1109986
{txt}{space 18}var(_cons) {c |}{res}{col 33} .8266073{col 44} .2459877{col 58} .4613097{col 70} 1.481173
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}   4.9856{col 44} .3947674{col 58} 4.268919{col 70} 5.822598
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay CON4:CON4}}{col 14}{c |}{res}{col 16}    28,867{col 28}        .{col 39}-64212.49{col 50}    20{col 58}   128465{col 69} 128630.4
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Export table (Table S8 is modified from this exported table)
. esttab CON* using "~/Desktop/CPS_replication/tables/Table S8.tex", ///
>         replace se b(4) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table S8.tex"'})

{com}. eststo clear
{txt}
{com}. 
. *** Table S9 ***
. // Require ado files "cgmwildboot.ado" and "cgmreg.ado"
. // Model 1
. eststo WB1: cgmwildboot diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr, cluster(code) ///
>         bootcluster(code) seed(1234567) reps(400)
{txt}Bootstrap reps ({res}400{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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..................................................   400
.
Regress with clustered SEs/Wild bootstrap ({res}400{txt} successful resamples)
Number of clustvars{col 20}={col 21}{res}    1{txt}{col 50}Number of obs{col 64}={col 66}{res}   30414
{txt}Num combinations{col 20}={col 21}{res}    1{txt}{col 50}R-squared{col 64}={col 66}{res}  0.1534
{txt}{col 50}Adj R-squared{col 64}={col 66}{res}  0.1530
{col 50}{txt}G(code){col 64}={col 66}{res}      22
{col 50}{txt}(Bootstrapped)
{hline 12}{c TT}{hline 60}
diff_dem_v~m{c |}       Coef.        Null     p-value    [95% Conf. Interval]
{hline 12}{c +}{hline 60}
    new_fotp{c |}{res}{col 16}-.07871801{col 28}         .{col 40}       .08{col 52}-.13125828{col 64} -.0226396
  {txt}university{c |}{res}{col 16}-.66588537{col 28}         .{col 40}      .005{col 52}-.91618448{col 64}-.43245924
      {txt}female{c |}{res}{col 16} .10323453{col 28}         .{col 40}      .115{col 52}-.01430268{col 64} .22844794
         {txt}age{c |}{res}{col 16}-.00665615{col 28}         .{col 40}       .57{col 52}-.02626421{col 64} .01602471
      {txt}age_sq{c |}{res}{col 16} .00331328{col 28}         .{col 40}        .8{col 52}-.02090809{col 64} .02397602
     {txt}married{c |}{res}{col 16}  .2614308{col 28}         .{col 40}      .245{col 52}-.09380188{col 64} .60616446
  {txt}unemployed{c |}{res}{col 16}-.24992882{col 28}         .{col 40}      .205{col 52}-.56687635{col 64} .06336742
      {txt}income{c |}{res}{col 16} .28970629{col 28}         .{col 40}         0{col 52} .17789964{col 64} .39650363
{txt}social_class{c |}{res}{col 16}-.10011918{col 28}         .{col 40}      .285{col 52}-.24778041{col 64} .07958958
      {txt}ln_gdp{c |}{res}{col 16} .06687554{col 28}         .{col 40}        .8{col 52}  -.396934{col 64} .50208455
{txt}growth_one~r{c |}{res}{col 16} .04783263{col 28}         .{col 40}       .01{col 52} .01446826{col 64} .08383957
        {txt}cons{c |}{res}{col 16} 2.3340344{col 28}         .{col 40}       .51{col 52}-1.7531496{col 64} 6.4968114
{txt}{hline 12}{c BT}{hline 60}

{com}. 
. // Model 2
. eststo WB2: cgmwildboot diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov, cluster(code) ///
>         bootcluster(code) seed(1234567) reps(400)
{txt}Bootstrap reps ({res}400{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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..................................................   250
..................................................   300
..................................................   350
..................................................   400
.
Regress with clustered SEs/Wild bootstrap ({res}400{txt} successful resamples)
Number of clustvars{col 20}={col 21}{res}    1{txt}{col 50}Number of obs{col 64}={col 66}{res}   30414
{txt}Num combinations{col 20}={col 21}{res}    1{txt}{col 50}R-squared{col 64}={col 66}{res}  0.2278
{txt}{col 50}Adj R-squared{col 64}={col 66}{res}  0.2275
{col 50}{txt}G(code){col 64}={col 66}{res}      22
{col 50}{txt}(Bootstrapped)
{hline 12}{c TT}{hline 60}
diff_dem_v~m{c |}       Coef.        Null     p-value    [95% Conf. Interval]
{hline 12}{c +}{hline 60}
    new_fotp{c |}{res}{col 16}-.10718656{col 28}         .{col 40}       .01{col 52}-.15631334{col 64}-.05902862
  {txt}university{c |}{res}{col 16}-.52997207{col 28}         .{col 40}      .005{col 52}-.70675284{col 64}-.32749477
      {txt}female{c |}{res}{col 16} .13263808{col 28}         .{col 40}      .045{col 52} .01044593{col 64} .24995711
         {txt}age{c |}{res}{col 16} -.0136059{col 28}         .{col 40}       .07{col 52}-.02651908{col 64} .00056194
      {txt}age_sq{c |}{res}{col 16} .01328441{col 28}         .{col 40}      .105{col 52}-.00234844{col 64} .02830522
     {txt}married{c |}{res}{col 16} .19672628{col 28}         .{col 40}       .17{col 52}-.02906442{col 64} .42974913
  {txt}unemployed{c |}{res}{col 16}-.11751725{col 28}         .{col 40}       .53{col 52}-.40000632{col 64} .18687329
      {txt}income{c |}{res}{col 16} .21744549{col 28}         .{col 40}         0{col 52} .12675819{col 64} .29541761
{txt}social_class{c |}{res}{col 16}-.02567651{col 28}         .{col 40}      .715{col 52}-.14613783{col 64} .10898737
      {txt}ln_gdp{c |}{res}{col 16}-.66304144{col 28}         .{col 40}       .03{col 52} -1.141646{col 64}-.23927759
{txt}growth_one~r{c |}{res}{col 16}-.00742404{col 28}         .{col 40}       .69{col 52}-.04612705{col 64} .03145215
     {txt}new_rol{c |}{res}{col 16} .02191686{col 28}         .{col 40}       .68{col 52}-.05462635{col 64} .10150718
     {txt}new_gov{c |}{res}{col 16} .03956767{col 28}         .{col 40}      .345{col 52} -.0277112{col 64} .10091451
        {txt}cons{c |}{res}{col 16} 7.7484391{col 28}         .{col 40}         0{col 52} 4.2163672{col 64} 11.588861
{txt}{hline 12}{c BT}{hline 60}

{com}. 
. // Model 3
. eststo WB3: cgmwildboot diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr, cluster(code) ///
>         bootcluster(code) seed(1234567) reps(400)
{txt}Bootstrap reps ({res}400{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
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..................................................   200
..................................................   250
..................................................   300
..................................................   350
..................................................   400
.
Regress with clustered SEs/Wild bootstrap ({res}400{txt} successful resamples)
Number of clustvars{col 20}={col 21}{res}    1{txt}{col 50}Number of obs{col 64}={col 66}{res}   30414
{txt}Num combinations{col 20}={col 21}{res}    1{txt}{col 50}R-squared{col 64}={col 66}{res}  0.1898
{txt}{col 50}Adj R-squared{col 64}={col 66}{res}  0.1895
{col 50}{txt}G(code){col 64}={col 66}{res}      22
{col 50}{txt}(Bootstrapped)
{hline 12}{c TT}{hline 60}
diff_dem_v~m{c |}       Coef.        Null     p-value    [95% Conf. Interval]
{hline 12}{c +}{hline 60}
     new_msf{c |}{res}{col 16}-.07533345{col 28}         .{col 40}       .01{col 52} -.1057549{col 64}-.03767006
  {txt}university{c |}{res}{col 16}-.64840477{col 28}         .{col 40}      .005{col 52}-.85156268{col 64}-.40712422
      {txt}female{c |}{res}{col 16} .08331704{col 28}         .{col 40}      .305{col 52}-.05529396{col 64} .21776032
         {txt}age{c |}{res}{col 16}-.01090897{col 28}         .{col 40}      .235{col 52}-.02673488{col 64} .00968852
      {txt}age_sq{c |}{res}{col 16} .00790982{col 28}         .{col 40}       .39{col 52}-.01146844{col 64} .02443356
     {txt}married{c |}{res}{col 16} .23640855{col 28}         .{col 40}      .255{col 52}-.12886749{col 64} .57632434
  {txt}unemployed{c |}{res}{col 16}-.28463901{col 28}         .{col 40}       .12{col 52}-.58129478{col 64} .00901781
      {txt}income{c |}{res}{col 16} .28914668{col 28}         .{col 40}         0{col 52} .20495476{col 64} .37982821
{txt}social_class{c |}{res}{col 16}-.04421418{col 28}         .{col 40}      .515{col 52}-.17529792{col 64} .13258299
      {txt}ln_gdp{c |}{res}{col 16}-.32672021{col 28}         .{col 40}       .27{col 52}-.77770156{col 64} .12128501
{txt}growth_one~r{c |}{res}{col 16} .00035249{col 28}         .{col 40}      .995{col 52}-.03521551{col 64}  .0410652
        {txt}cons{c |}{res}{col 16} 6.7982303{col 28}         .{col 40}      .045{col 52} 1.6170986{col 64} 11.579651
{txt}{hline 12}{c BT}{hline 60}

{com}. 
. // Model 4
. eststo WB4: cgmwildboot diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov, cluster(code) ///
>         bootcluster(code) seed(1234567) reps(400)
{txt}Bootstrap reps ({res}400{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
.................................................    50
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..................................................   300
..................................................   350
..................................................   400
.
Regress with clustered SEs/Wild bootstrap ({res}400{txt} successful resamples)
Number of clustvars{col 20}={col 21}{res}    1{txt}{col 50}Number of obs{col 64}={col 66}{res}   30414
{txt}Num combinations{col 20}={col 21}{res}    1{txt}{col 50}R-squared{col 64}={col 66}{res}  0.2296
{txt}{col 50}Adj R-squared{col 64}={col 66}{res}  0.2293
{col 50}{txt}G(code){col 64}={col 66}{res}      22
{col 50}{txt}(Bootstrapped)
{hline 12}{c TT}{hline 60}
diff_dem_v~m{c |}       Coef.        Null     p-value    [95% Conf. Interval]
{hline 12}{c +}{hline 60}
     new_msf{c |}{res}{col 16}-.07332569{col 28}         .{col 40}      .005{col 52}-.09391408{col 64}-.04905408
  {txt}university{c |}{res}{col 16}-.53018178{col 28}         .{col 40}      .005{col 52}-.70899755{col 64}-.32445014
      {txt}female{c |}{res}{col 16} .11606435{col 28}         .{col 40}       .07{col 52}-.01084836{col 64} .23845622
         {txt}age{c |}{res}{col 16}-.01644315{col 28}         .{col 40}       .06{col 52}-.03106582{col 64}-.00192454
      {txt}age_sq{c |}{res}{col 16} .01548175{col 28}         .{col 40}       .07{col 52}-.00002393{col 64}  .0310015
     {txt}married{c |}{res}{col 16} .13498965{col 28}         .{col 40}      .405{col 52}-.12216493{col 64} .39760649
  {txt}unemployed{c |}{res}{col 16}-.19602731{col 28}         .{col 40}       .19{col 52}-.48295727{col 64} .06463929
      {txt}income{c |}{res}{col 16} .22647094{col 28}         .{col 40}         0{col 52}  .1475828{col 64} .29733834
{txt}social_class{c |}{res}{col 16}-.02654749{col 28}         .{col 40}       .55{col 52}-.11032747{col 64} .07762059
      {txt}ln_gdp{c |}{res}{col 16}-.83170291{col 28}         .{col 40}      .005{col 52}-1.3363237{col 64} -.3363542
{txt}growth_one~r{c |}{res}{col 16}-.04669633{col 28}         .{col 40}       .11{col 52}-.09450416{col 64} .00150325
     {txt}new_rol{c |}{res}{col 16}-.03462643{col 28}         .{col 40}      .465{col 52}-.09978979{col 64} .02530923
     {txt}new_gov{c |}{res}{col 16} .06843883{col 28}         .{col 40}      .075{col 52} .01600225{col 64} .12845901
        {txt}cons{c |}{res}{col 16} 10.303866{col 28}         .{col 40}         0{col 52} 5.6452723{col 64} 14.754936
{txt}{hline 12}{c BT}{hline 60}

{com}. 
. // Export table (Table S9 is modified from this exported table)
. esttab WB* using "~/Desktop/CPS_replication/tables/Table S9.tex", ///
>         replace se b(4) ar2 star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table S9.tex"'})

{com}. eststo clear
{txt}
{com}. 
. *** Table S4 ***
. // Model 1
. eststo MLR1: melogit overest_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-17740.778}  
Iteration 1:{space 3}log likelihood = {res:-17735.575}  
Iteration 2:{space 3}log likelihood = {res:-17735.574}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-15546.993}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-15546.993}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-15533.747}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-15528.146}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-15525.894}  
Iteration 4:{space 3}log likelihood = {res:-15496.298}  
Iteration 5:{space 3}log likelihood = {res: -15460.13}  
Iteration 6:{space 3}log likelihood = {res:-15460.055}  
Iteration 7:{space 3}log likelihood = {res:-15460.055}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={res}{col 69}        22

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       854
{col 63}{txt}avg{col 67}={res}{col 69}   1,382.5
{col 63}{txt}max{col 67}={res}{col 69}     1,997

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   467.41
{txt}Log likelihood = {res}-15460.055{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   overest_vdem{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}new_fotp {c |}{col 17}{res}{space 2}-.0610443{col 29}{space 2} .0254031{col 40}{space 1}   -2.40{col 49}{space 3}0.016{col 57}{space 4}-.1108333{col 70}{space 3}-.0112552
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3304394{col 29}{space 2} .0583759{col 40}{space 1}   -5.66{col 49}{space 3}0.000{col 57}{space 4}-.4448541{col 70}{space 3}-.2160248
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1482769{col 29}{space 2} .0283009{col 40}{space 1}    5.24{col 49}{space 3}0.000{col 57}{space 4} .0928081{col 70}{space 3} .2037458
{txt}{space 12}age {c |}{col 17}{res}{space 2}  -.01664{col 29}{space 2} .0051913{col 40}{space 1}   -3.21{col 49}{space 3}0.001{col 57}{space 4}-.0268148{col 70}{space 3}-.0064652
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0203923{col 29}{space 2} .0056224{col 40}{space 1}    3.63{col 49}{space 3}0.000{col 57}{space 4} .0093726{col 70}{space 3} .0314121
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0312071{col 29}{space 2} .0326721{col 40}{space 1}    0.96{col 49}{space 3}0.339{col 57}{space 4}-.0328291{col 70}{space 3} .0952433
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0959618{col 29}{space 2} .0522852{col 40}{space 1}   -1.84{col 49}{space 3}0.066{col 57}{space 4}-.1984389{col 70}{space 3} .0065153
{txt}{space 9}income {c |}{col 17}{res}{space 2}  .153778{col 29}{space 2} .0084382{col 40}{space 1}   18.22{col 49}{space 3}0.000{col 57}{space 4} .1372394{col 70}{space 3} .1703166
{txt}{space 3}social_class {c |}{col 17}{res}{space 2}-.0222373{col 29}{space 2} .0174767{col 40}{space 1}   -1.27{col 49}{space 3}0.203{col 57}{space 4} -.056491{col 70}{space 3} .0120163
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2} .0704223{col 29}{space 2} .2590328{col 40}{space 1}    0.27{col 49}{space 3}0.786{col 57}{space 4}-.4372726{col 70}{space 3} .5781172
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}  .036907{col 29}{space 2} .0361824{col 40}{space 1}    1.02{col 49}{space 3}0.308{col 57}{space 4}-.0340092{col 70}{space 3} .1078232
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  1.73066{col 29}{space 2} 2.636229{col 40}{space 1}    0.66{col 49}{space 3}0.512{col 57}{space 4}-3.436255{col 70}{space 3} 6.897574
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}code           {col 17}{txt}{c |}
{space 1}var(university){c |}{col 17}{res}{space 2} .0312304{col 29}{space 2} .0196357{col 57}{space 4} .0091074{col 70}{space 3} .1070927
{txt}{space 6}var(_cons){c |}{col 17}{res}{space 2} 1.221903{col 29}{space 2} .3722544{col 57}{space 4} .6725385{col 70}{space 3} 2.220018
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 4551.04{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MLR1:MLR1}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-15460.06{col 50}    14{col 58} 30948.11{col 69} 31064.63
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 2
. eststo MLR2: melogit overest_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -16637.68}  
Iteration 1:{space 3}log likelihood = {res:-16601.597}  
Iteration 2:{space 3}log likelihood = {res:-16601.454}  
Iteration 3:{space 3}log likelihood = {res:-16601.454}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-15490.999}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-15490.999}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-15474.727}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-15472.318}  (backed up)
Iteration 3:{space 3}log likelihood = {res: -15466.76}  
Iteration 4:{space 3}log likelihood = {res:-15455.536}  
Iteration 5:{space 3}log likelihood = {res:-15452.048}  
Iteration 6:{space 3}log likelihood = {res:-15452.048}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={res}{col 69}        22

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       854
{col 63}{txt}avg{col 67}={res}{col 69}   1,382.5
{col 63}{txt}max{col 67}={res}{col 69}     1,997

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   500.17
{txt}Log likelihood = {res}-15452.048{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   overest_vdem{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}new_fotp {c |}{col 17}{res}{space 2}-.0872086{col 29}{space 2}  .019739{col 40}{space 1}   -4.42{col 49}{space 3}0.000{col 57}{space 4}-.1258963{col 70}{space 3}-.0485208
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3297838{col 29}{space 2} .0585363{col 40}{space 1}   -5.63{col 49}{space 3}0.000{col 57}{space 4}-.4445128{col 70}{space 3}-.2150548
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1482701{col 29}{space 2} .0283006{col 40}{space 1}    5.24{col 49}{space 3}0.000{col 57}{space 4} .0928019{col 70}{space 3} .2037383
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0166402{col 29}{space 2} .0051913{col 40}{space 1}   -3.21{col 49}{space 3}0.001{col 57}{space 4} -.026815{col 70}{space 3}-.0064653
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0203839{col 29}{space 2} .0056225{col 40}{space 1}    3.63{col 49}{space 3}0.000{col 57}{space 4}  .009364{col 70}{space 3} .0314037
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0310758{col 29}{space 2} .0326692{col 40}{space 1}    0.95{col 49}{space 3}0.341{col 57}{space 4}-.0329546{col 70}{space 3} .0951063
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} -.095084{col 29}{space 2} .0522785{col 40}{space 1}   -1.82{col 49}{space 3}0.069{col 57}{space 4} -.197548{col 70}{space 3} .0073799
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1537282{col 29}{space 2} .0084371{col 40}{space 1}   18.22{col 49}{space 3}0.000{col 57}{space 4} .1371917{col 70}{space 3} .1702646
{txt}{space 3}social_class {c |}{col 17}{res}{space 2}-.0221321{col 29}{space 2} .0174758{col 40}{space 1}   -1.27{col 49}{space 3}0.205{col 57}{space 4} -.056384{col 70}{space 3} .0121197
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.6551054{col 29}{space 2} .2396634{col 40}{space 1}   -2.73{col 49}{space 3}0.006{col 57}{space 4}-1.124837{col 70}{space 3}-.1853738
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0196314{col 29}{space 2} .0296285{col 40}{space 1}   -0.66{col 49}{space 3}0.508{col 57}{space 4}-.0777022{col 70}{space 3} .0384394
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2} .0146964{col 29}{space 2}  .032258{col 40}{space 1}    0.46{col 49}{space 3}0.649{col 57}{space 4}-.0485282{col 70}{space 3} .0779209
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2} .0485084{col 29}{space 2} .0291667{col 40}{space 1}    1.66{col 49}{space 3}0.096{col 57}{space 4}-.0086572{col 70}{space 3}  .105674
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  6.72437{col 29}{space 2} 2.145912{col 40}{space 1}    3.13{col 49}{space 3}0.002{col 57}{space 4}  2.51846{col 70}{space 3} 10.93028
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}code           {col 17}{txt}{c |}
{space 1}var(university){c |}{col 17}{res}{space 2} .0315935{col 29}{space 2}  .019822{col 57}{space 4} .0092374{col 70}{space 3} .1080558
{txt}{space 6}var(_cons){c |}{col 17}{res}{space 2} .5882944{col 29}{space 2} .1798121{col 57}{space 4} .3231656{col 70}{space 3} 1.070938
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 2298.81{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MLR2:MLR2}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-15452.05{col 50}    16{col 58}  30936.1{col 69} 31069.26
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 3
. eststo MLR3: melogit overest_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-17323.491}  
Iteration 1:{space 3}log likelihood = {res:-17319.185}  
Iteration 2:{space 3}log likelihood = {res:-17319.185}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-15555.347}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-15555.347}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-15543.074}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-15531.039}  
Iteration 3:{space 3}log likelihood = {res:-15459.173}  
Iteration 4:{space 3}log likelihood = {res: -15457.92}  
Iteration 5:{space 3}log likelihood = {res:-15457.801}  
Iteration 6:{space 3}log likelihood = {res:-15457.799}  
Iteration 7:{space 3}log likelihood = {res:-15457.799}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={res}{col 69}        22

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       854
{col 63}{txt}avg{col 67}={res}{col 69}   1,382.5
{col 63}{txt}max{col 67}={res}{col 69}     1,997

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   474.32
{txt}Log likelihood = {res}-15457.799{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   overest_vdem{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}new_msf {c |}{col 17}{res}{space 2}-.0578652{col 29}{space 2} .0166516{col 40}{space 1}   -3.48{col 49}{space 3}0.001{col 57}{space 4}-.0905017{col 70}{space 3}-.0252286
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3308726{col 29}{space 2} .0585485{col 40}{space 1}   -5.65{col 49}{space 3}0.000{col 57}{space 4}-.4456255{col 70}{space 3}-.2161196
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1481055{col 29}{space 2} .0283014{col 40}{space 1}    5.23{col 49}{space 3}0.000{col 57}{space 4} .0926358{col 70}{space 3} .2035752
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0166367{col 29}{space 2} .0051912{col 40}{space 1}   -3.20{col 49}{space 3}0.001{col 57}{space 4}-.0268113{col 70}{space 3}-.0064622
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0203831{col 29}{space 2} .0056223{col 40}{space 1}    3.63{col 49}{space 3}0.000{col 57}{space 4} .0093636{col 70}{space 3} .0314025
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0311969{col 29}{space 2} .0326712{col 40}{space 1}    0.95{col 49}{space 3}0.340{col 57}{space 4}-.0328375{col 70}{space 3} .0952313
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0963262{col 29}{space 2} .0522829{col 40}{space 1}   -1.84{col 49}{space 3}0.065{col 57}{space 4}-.1987989{col 70}{space 3} .0061464
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1538928{col 29}{space 2} .0084385{col 40}{space 1}   18.24{col 49}{space 3}0.000{col 57}{space 4} .1373536{col 70}{space 3} .1704319
{txt}{space 3}social_class {c |}{col 17}{res}{space 2}-.0221845{col 29}{space 2} .0174759{col 40}{space 1}   -1.27{col 49}{space 3}0.204{col 57}{space 4}-.0564368{col 70}{space 3} .0120677
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.2254816{col 29}{space 2} .2585757{col 40}{space 1}   -0.87{col 49}{space 3}0.383{col 57}{space 4}-.7322806{col 70}{space 3} .2813174
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2} .0052584{col 29}{space 2} .0348457{col 40}{space 1}    0.15{col 49}{space 3}0.880{col 57}{space 4} -.063038{col 70}{space 3} .0735548
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 5.046955{col 29}{space 2} 2.771894{col 40}{space 1}    1.82{col 49}{space 3}0.069{col 57}{space 4}-.3858576{col 70}{space 3} 10.47977
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}code           {col 17}{txt}{c |}
{space 1}var(university){c |}{col 17}{res}{space 2}  .031586{col 29}{space 2} .0197903{col 57}{space 4} .0092507{col 70}{space 3} .1078491
{txt}{space 6}var(_cons){c |}{col 17}{res}{space 2} .9938761{col 29}{space 2} .3032415{col 57}{space 4} .5465403{col 70}{space 3}  1.80735
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 3722.77{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MLR3:MLR3}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39} -15457.8{col 50}    14{col 58}  30943.6{col 69} 31060.11
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 4
. eststo MLR4: melogit overest_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -16610.04}  
Iteration 1:{space 3}log likelihood = {res:-16580.859}  
Iteration 2:{space 3}log likelihood = {res:-16580.772}  
Iteration 3:{space 3}log likelihood = {res:-16580.772}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-15495.788}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-15495.788}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-15480.555}  (not concave)
Iteration 2:{space 3}log likelihood = {res: -15475.79}  
Iteration 3:{space 3}log likelihood = {res:-15469.475}  
Iteration 4:{space 3}log likelihood = {res:  -15456.1}  
Iteration 5:{space 3}log likelihood = {res:-15451.825}  
Iteration 6:{space 3}log likelihood = {res:-15451.825}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={res}{col 69}        22

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       854
{col 63}{txt}avg{col 67}={res}{col 69}   1,382.5
{col 63}{txt}max{col 67}={res}{col 69}     1,997

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}13{txt}){col 67}={res}{col 70}   501.46
{txt}Log likelihood = {res}-15451.825{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   overest_vdem{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}new_msf {c |}{col 17}{res}{space 2} -.057593{col 29}{space 2}  .012753{col 40}{space 1}   -4.52{col 49}{space 3}0.000{col 57}{space 4}-.0825883{col 70}{space 3}-.0325977
{txt}{space 5}university {c |}{col 17}{res}{space 2}  -.33024{col 29}{space 2} .0586457{col 40}{space 1}   -5.63{col 49}{space 3}0.000{col 57}{space 4}-.4451834{col 70}{space 3}-.2152965
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1481042{col 29}{space 2} .0283014{col 40}{space 1}    5.23{col 49}{space 3}0.000{col 57}{space 4} .0926345{col 70}{space 3} .2035738
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0166524{col 29}{space 2} .0051911{col 40}{space 1}   -3.21{col 49}{space 3}0.001{col 57}{space 4}-.0268268{col 70}{space 3}-.0064781
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0203901{col 29}{space 2} .0056222{col 40}{space 1}    3.63{col 49}{space 3}0.000{col 57}{space 4} .0093708{col 70}{space 3} .0314093
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0307317{col 29}{space 2} .0326686{col 40}{space 1}    0.94{col 49}{space 3}0.347{col 57}{space 4}-.0332975{col 70}{space 3} .0947609
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0959311{col 29}{space 2} .0522791{col 40}{space 1}   -1.83{col 49}{space 3}0.067{col 57}{space 4}-.1983962{col 70}{space 3} .0065341
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1538119{col 29}{space 2} .0084376{col 40}{space 1}   18.23{col 49}{space 3}0.000{col 57}{space 4} .1372745{col 70}{space 3} .1703493
{txt}{space 3}social_class {c |}{col 17}{res}{space 2}-.0222498{col 29}{space 2} .0174758{col 40}{space 1}   -1.27{col 49}{space 3}0.203{col 57}{space 4}-.0565017{col 70}{space 3} .0120022
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.8083211{col 29}{space 2} .2471089{col 40}{space 1}   -3.27{col 49}{space 3}0.001{col 57}{space 4}-1.292646{col 70}{space 3}-.3239965
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0475027{col 29}{space 2} .0306522{col 40}{space 1}   -1.55{col 49}{space 3}0.121{col 57}{space 4}-.1075799{col 70}{space 3} .0125745
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2}-.0283583{col 29}{space 2} .0297205{col 40}{space 1}   -0.95{col 49}{space 3}0.340{col 57}{space 4}-.0866094{col 70}{space 3} .0298929
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2} .0705608{col 29}{space 2} .0281339{col 40}{space 1}    2.51{col 49}{space 3}0.012{col 57}{space 4} .0154195{col 70}{space 3} .1257021
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 8.706912{col 29}{space 2} 2.338879{col 40}{space 1}    3.72{col 49}{space 3}0.000{col 57}{space 4} 4.122793{col 70}{space 3} 13.29103
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}code           {col 17}{txt}{c |}
{space 1}var(university){c |}{col 17}{res}{space 2} .0318137{col 29}{space 2} .0199306{col 57}{space 4} .0093186{col 70}{space 3} .1086113
{txt}{space 6}var(_cons){c |}{col 17}{res}{space 2} .5754147{col 29}{space 2} .1761991{col 57}{space 4} .3157421{col 70}{space 3} 1.048647
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 2257.89{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay MLR4:MLR4}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-15451.83{col 50}    16{col 58} 30935.65{col 69} 31068.81
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Export table (Table S4 is modified from this exported table)
. esttab MLR* using "~/Desktop/CPS_replication/tables/Table S4.tex", ///
>         replace se b(4) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table S4.tex"'})

{com}. 
. *** Table S5 ***
. // Model 1
. eststo HR1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr HRPS || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68636.248}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68636.248}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}12{txt}){col 67}={col 70}{res}   520.70
{txt}Log pseudolikelihood = {res}-68636.248{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0895954{col 27}{space 2} .0373938{col 38}{space 1}   -2.40{col 47}{space 3}0.017{col 55}{space 4}-.1628858{col 68}{space 3} -.016305
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3990966{col 27}{space 2} .0584778{col 38}{space 1}   -6.82{col 47}{space 3}0.000{col 55}{space 4} -.513711{col 68}{space 3}-.2844821
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1631817{col 27}{space 2} .0545877{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0561917{col 68}{space 3} .2701717
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215535{col 27}{space 2} .0044222{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4}-.0302208{col 68}{space 3}-.0128861
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2}  .026185{col 27}{space 2} .0052095{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0159746{col 68}{space 3} .0363954
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0301896{col 27}{space 2} .0593536{col 38}{space 1}    0.51{col 47}{space 3}0.611{col 55}{space 4}-.0861414{col 68}{space 3} .1465206
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1384841{col 27}{space 2} .0860333{col 38}{space 1}   -1.61{col 47}{space 3}0.107{col 55}{space 4}-.3071064{col 68}{space 3} .0301381
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1630411{col 27}{space 2} .0309298{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1024199{col 68}{space 3} .2236623
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0104129{col 27}{space 2} .0380597{col 38}{space 1}    0.27{col 47}{space 3}0.784{col 55}{space 4}-.0641828{col 68}{space 3} .0850086
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.0026371{col 27}{space 2} .3194792{col 38}{space 1}   -0.01{col 47}{space 3}0.993{col 55}{space 4}-.6288047{col 68}{space 3} .6235306
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0290505{col 27}{space 2}  .029073{col 38}{space 1}    1.00{col 47}{space 3}0.318{col 55}{space 4}-.0279315{col 68}{space 3} .0860326
{txt}{space 9}HRPS {c |}{col 15}{res}{space 2} .3690273{col 27}{space 2} .3654666{col 38}{space 1}    1.01{col 47}{space 3}0.313{col 55}{space 4}-.3472741{col 68}{space 3} 1.085329
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.999213{col 27}{space 2} 3.441635{col 38}{space 1}    1.16{col 47}{space 3}0.245{col 55}{space 4}-2.746268{col 68}{space 3} 10.74469
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0489149{col 44}  .028866{col 58}  .015386{col 70} .1555097
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.575088{col 44} .3380823{col 58} 1.034189{col 70} 2.398886
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315282{col 44} .4072899{col 58} 4.574061{col 70} 6.176617
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HR1:HR1}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68636.25{col 50}    16{col 58} 137304.5{col 69} 137437.7
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 2
. eststo HR2: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov HRPS || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -68631.14}  
Iteration 1:{space 3}log pseudolikelihood = {res: -68631.14}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}14{txt}){col 67}={col 70}{res}   459.81
{txt}Log pseudolikelihood = {res} -68631.14{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1043799{col 27}{space 2} .0238958{col 38}{space 1}   -4.37{col 47}{space 3}0.000{col 55}{space 4}-.1512148{col 68}{space 3} -.057545
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3989914{col 27}{space 2}  .058529{col 38}{space 1}   -6.82{col 47}{space 3}0.000{col 55}{space 4}-.5137062{col 68}{space 3}-.2842767
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1631765{col 27}{space 2} .0545505{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0562594{col 68}{space 3} .2700936
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215579{col 27}{space 2} .0044184{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-.0302179{col 68}{space 3} -.012898
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261903{col 27}{space 2} .0052068{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0159852{col 68}{space 3} .0363955
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0296931{col 27}{space 2} .0594072{col 38}{space 1}    0.50{col 47}{space 3}0.617{col 55}{space 4}-.0867429{col 68}{space 3} .1461291
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1374855{col 27}{space 2}  .085996{col 38}{space 1}   -1.60{col 47}{space 3}0.110{col 55}{space 4}-.3060347{col 68}{space 3} .0310636
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1629781{col 27}{space 2} .0309394{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4}  .102338{col 68}{space 3} .2236181
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0106155{col 27}{space 2} .0380541{col 38}{space 1}    0.28{col 47}{space 3}0.780{col 55}{space 4}-.0639692{col 68}{space 3} .0852001
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5900123{col 27}{space 2} .2563754{col 38}{space 1}   -2.30{col 47}{space 3}0.021{col 55}{space 4}-1.092499{col 68}{space 3}-.0875257
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0049592{col 27}{space 2} .0246205{col 38}{space 1}   -0.20{col 47}{space 3}0.840{col 55}{space 4}-.0532145{col 68}{space 3} .0432961
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0399702{col 27}{space 2} .0497782{col 38}{space 1}    0.80{col 47}{space 3}0.422{col 55}{space 4}-.0575933{col 68}{space 3} .1375336
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0347278{col 27}{space 2} .0332374{col 38}{space 1}    1.04{col 47}{space 3}0.296{col 55}{space 4}-.0304164{col 68}{space 3}  .099872
{txt}{space 9}HRPS {c |}{col 15}{res}{space 2}-.2691426{col 27}{space 2} .3638859{col 38}{space 1}   -0.74{col 47}{space 3}0.460{col 55}{space 4}-.9823457{col 68}{space 3} .4440606
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.734306{col 27}{space 2} 2.000351{col 38}{space 1}    3.37{col 47}{space 3}0.001{col 55}{space 4}  2.81369{col 68}{space 3} 10.65492
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0491642{col 44} .0290863{col 58} .0154193{col 70}  .156759
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9879789{col 44} .2049374{col 58} .6579337{col 70} 1.483588
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315272{col 44} .4072835{col 58} 4.574062{col 70} 6.176593
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HR2:HR2}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68631.14{col 50}    18{col 58} 137298.3{col 69} 137448.1
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 3
. eststo HR3: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr HRPS || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68634.582}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68634.582}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}12{txt}){col 67}={col 70}{res}   370.42
{txt}Log pseudolikelihood = {res}-68634.582{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0716323{col 27}{space 2} .0191122{col 38}{space 1}   -3.75{col 47}{space 3}0.000{col 55}{space 4}-.1090916{col 68}{space 3} -.034173
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3994116{col 27}{space 2} .0584923{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5140545{col 68}{space 3}-.2847688
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1630502{col 27}{space 2} .0545857{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0560641{col 68}{space 3} .2700362
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215564{col 27}{space 2} .0044248{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4} -.030229{col 68}{space 3}-.0128839
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261847{col 27}{space 2} .0052112{col 38}{space 1}    5.02{col 47}{space 3}0.000{col 55}{space 4} .0159708{col 68}{space 3} .0363985
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0300711{col 27}{space 2} .0593716{col 38}{space 1}    0.51{col 47}{space 3}0.613{col 55}{space 4}-.0862952{col 68}{space 3} .1464374
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1386531{col 27}{space 2} .0860245{col 38}{space 1}   -1.61{col 47}{space 3}0.107{col 55}{space 4} -.307258{col 68}{space 3} .0299519
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1631258{col 27}{space 2} .0309135{col 38}{space 1}    5.28{col 47}{space 3}0.000{col 55}{space 4} .1025365{col 68}{space 3} .2237151
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0104885{col 27}{space 2} .0380594{col 38}{space 1}    0.28{col 47}{space 3}0.783{col 55}{space 4}-.0641066{col 68}{space 3} .0850837
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.2870147{col 27}{space 2} .3273276{col 38}{space 1}   -0.88{col 47}{space 3}0.381{col 55}{space 4}-.9285649{col 68}{space 3} .3545356
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0004987{col 27}{space 2} .0288508{col 38}{space 1}   -0.02{col 47}{space 3}0.986{col 55}{space 4}-.0570452{col 68}{space 3} .0560478
{txt}{space 9}HRPS {c |}{col 15}{res}{space 2} .1422006{col 27}{space 2} .3115132{col 38}{space 1}    0.46{col 47}{space 3}0.648{col 55}{space 4}-.4683541{col 68}{space 3} .7527552
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.851775{col 27}{space 2}  3.45587{col 38}{space 1}    1.98{col 47}{space 3}0.047{col 55}{space 4} .0783946{col 68}{space 3} 13.62515
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0492025{col 44} .0290991{col 58} .0154374{col 70} .1568199
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.353043{col 44} .2829049{col 58} .8981229{col 70} 2.038391
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.31527{col 44} .4072886{col 58} 4.574051{col 70} 6.176602
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HR3:HR3}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68634.58{col 50}    16{col 58} 137301.2{col 69} 137434.3
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Model 4
. eststo HR4: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov HRPS || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68630.978}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68630.978}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}14{txt}){col 67}={col 70}{res}   387.12
{txt}Log pseudolikelihood = {res}-68630.978{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0689491{col 27}{space 2} .0111032{col 38}{space 1}   -6.21{col 47}{space 3}0.000{col 55}{space 4}-.0907109{col 68}{space 3}-.0471872
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3993448{col 27}{space 2}  .058528{col 38}{space 1}   -6.82{col 47}{space 3}0.000{col 55}{space 4}-.5140575{col 68}{space 3}-.2846321
{txt}{space 7}female {c |}{col 15}{res}{space 2}  .163064{col 27}{space 2} .0545369{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0561737{col 68}{space 3} .2699544
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215706{col 27}{space 2}  .004422{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-.0302377{col 68}{space 3}-.0129036
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261995{col 27}{space 2} .0052084{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0159911{col 68}{space 3} .0364078
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0294909{col 27}{space 2}  .059442{col 38}{space 1}    0.50{col 47}{space 3}0.620{col 55}{space 4}-.0870132{col 68}{space 3}  .145995
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1380309{col 27}{space 2} .0859352{col 38}{space 1}   -1.61{col 47}{space 3}0.108{col 55}{space 4}-.3064609{col 68}{space 3}  .030399
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1630349{col 27}{space 2} .0309087{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1024548{col 68}{space 3} .2236149
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0105527{col 27}{space 2} .0380067{col 38}{space 1}    0.28{col 47}{space 3}0.781{col 55}{space 4}-.0639391{col 68}{space 3} .0850445
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7758808{col 27}{space 2} .2996418{col 38}{space 1}   -2.59{col 47}{space 3}0.010{col 55}{space 4}-1.363168{col 68}{space 3}-.1885937
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0388701{col 27}{space 2} .0299217{col 38}{space 1}   -1.30{col 47}{space 3}0.194{col 55}{space 4}-.0975155{col 68}{space 3} .0197754
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0129514{col 27}{space 2} .0496876{col 38}{space 1}   -0.26{col 47}{space 3}0.794{col 55}{space 4}-.1103372{col 68}{space 3} .0844344
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0616791{col 27}{space 2}  .034053{col 38}{space 1}    1.81{col 47}{space 3}0.070{col 55}{space 4}-.0050636{col 68}{space 3} .1284218
{txt}{space 9}HRPS {c |}{col 15}{res}{space 2}-.2461748{col 27}{space 2}  .345433{col 38}{space 1}   -0.71{col 47}{space 3}0.476{col 55}{space 4}-.9232111{col 68}{space 3} .4308615
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  9.16948{col 27}{space 2} 2.529108{col 38}{space 1}    3.63{col 47}{space 3}0.000{col 55}{space 4} 4.212519{col 68}{space 3} 14.12644
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0494126{col 44} .0292943{col 58} .0154597{col 70} .1579331
{txt}{space 18}var(_cons) {c |}{res}{col 33}  .973558{col 44} .2887225{col 58} .5444097{col 70} 1.740996
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315261{col 44} .4072829{col 58} 4.574052{col 70}  6.17658
{txt}{hline 29}{c BT}{hline 48}

{com}. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay HR4:HR4}}{col 14}{c |}{res}{col 16}    30,414{col 28}        .{col 39}-68630.98{col 50}    18{col 58}   137298{col 69} 137447.8
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. // Export table (Table S5 is modified from this exported table)
. esttab HR* using "~/Desktop/CPS_replication/tables/Table S5.tex", ///
>         replace se b(4) star(* 0.10 ** 0.05 *** 0.01)
{res}{txt}(output written to {browse  `"~/Desktop/CPS_replication/tables/Table S5.tex"'})

{com}. eststo clear
{txt}
{com}. 
. *** Other robustness checks ***
. ** References in "rob-check_summary.xlsx": rob_check >= 1 & rob_check <= 6
. /* Conduct placebo and horse race tests with free and fair elections (v2xel_frefair) */
. // Rescale v2xel_frefair so that it ranges from 0 to 100
. gen elec_frefair = v2xel_frefair * 100
{txt}
{com}. 
. // Model 1: placebo test (without media freedom)
. eststo FRE1: mixed diff_dem_vdem elec_frefair university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -68638.84}  
Iteration 1:{space 3}log pseudolikelihood = {res: -68638.84}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   267.75
{txt}Log pseudolikelihood = {res} -68638.84{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}elec_frefair {c |}{col 15}{res}{space 2}-.0241099{col 27}{space 2} .0150095{col 38}{space 1}   -1.61{col 47}{space 3}0.108{col 55}{space 4} -.053528{col 68}{space 3} .0053082
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3989662{col 27}{space 2} .0584336{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4} -.513494{col 68}{space 3}-.2844383
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1631712{col 27}{space 2} .0545917{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0561734{col 68}{space 3} .2701691
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215644{col 27}{space 2} .0044224{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-.0302321{col 68}{space 3}-.0128967
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261985{col 27}{space 2} .0052113{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0159845{col 68}{space 3} .0364126
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0300239{col 27}{space 2} .0593156{col 38}{space 1}    0.51{col 47}{space 3}0.613{col 55}{space 4}-.0862326{col 68}{space 3} .1462805
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1386003{col 27}{space 2} .0860621{col 38}{space 1}   -1.61{col 47}{space 3}0.107{col 55}{space 4} -.307279{col 68}{space 3} .0300783
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1630157{col 27}{space 2} .0309161{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1024213{col 68}{space 3}   .22361
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0104841{col 27}{space 2} .0380462{col 38}{space 1}    0.28{col 47}{space 3}0.783{col 55}{space 4}-.0640851{col 68}{space 3} .0850533
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2} .4214005{col 27}{space 2} .4110818{col 38}{space 1}    1.03{col 47}{space 3}0.305{col 55}{space 4} -.384305{col 68}{space 3} 1.227106
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0740866{col 27}{space 2}  .030566{col 38}{space 1}    2.42{col 47}{space 3}0.015{col 55}{space 4} .0141783{col 68}{space 3} .1339949
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-1.954096{col 27}{space 2} 3.690066{col 38}{space 1}   -0.53{col 47}{space 3}0.596{col 55}{space 4}-9.186493{col 68}{space 3}   5.2783
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0487915{col 44} .0287617{col 58} .0153666{col 70} .1549215
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.994936{col 44} .5476038{col 58} 1.164868{col 70} 3.416498
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315288{col 44} .4072948{col 58} 4.574059{col 70} 6.176634
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Model 2: placebo test (but with binary DV)
. eststo FRE2: melogit overest_vdem elec_frefair university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-18164.351}  
Iteration 1:{space 3}log likelihood = {res: -18154.34}  
Iteration 2:{space 3}log likelihood = {res:-18154.337}  
Iteration 3:{space 3}log likelihood = {res:-18154.337}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-15579.191}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-15579.191}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-15567.556}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-15555.842}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-15551.482}  
Iteration 4:{space 3}log likelihood = {res:-15462.319}  
Iteration 5:{space 3}log likelihood = {res:-15462.038}  
Iteration 6:{space 3}log likelihood = {res: -15462.03}  
Iteration 7:{space 3}log likelihood = {res: -15462.03}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={res}{col 69}        22

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       854
{col 63}{txt}avg{col 67}={res}{col 69}   1,382.5
{col 63}{txt}max{col 67}={res}{col 69}     1,997

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   462.37
{txt}Log likelihood = {res}-15462.03{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   overest_vdem{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}elec_frefair {c |}{col 17}{res}{space 2}-.0157251{col 29}{space 2} .0142639{col 40}{space 1}   -1.10{col 49}{space 3}0.270{col 57}{space 4}-.0436818{col 70}{space 3} .0122316
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3302973{col 29}{space 2} .0582665{col 40}{space 1}   -5.67{col 49}{space 3}0.000{col 57}{space 4}-.4444975{col 70}{space 3}-.2160971
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1482733{col 29}{space 2} .0283007{col 40}{space 1}    5.24{col 49}{space 3}0.000{col 57}{space 4} .0928049{col 70}{space 3} .2037417
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0166489{col 29}{space 2}  .005191{col 40}{space 1}   -3.21{col 49}{space 3}0.001{col 57}{space 4} -.026823{col 70}{space 3}-.0064748
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0204024{col 29}{space 2}  .005622{col 40}{space 1}    3.63{col 49}{space 3}0.000{col 57}{space 4} .0093834{col 70}{space 3} .0314214
{txt}{space 8}married {c |}{col 17}{res}{space 2}  .031335{col 29}{space 2} .0326718{col 40}{space 1}    0.96{col 49}{space 3}0.338{col 57}{space 4}-.0327004{col 70}{space 3} .0953705
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0962153{col 29}{space 2} .0522803{col 40}{space 1}   -1.84{col 49}{space 3}0.066{col 57}{space 4}-.1986828{col 70}{space 3} .0062521
{txt}{space 9}income {c |}{col 17}{res}{space 2}  .153741{col 29}{space 2} .0084385{col 40}{space 1}   18.22{col 49}{space 3}0.000{col 57}{space 4} .1372019{col 70}{space 3}   .17028
{txt}{space 3}social_class {c |}{col 17}{res}{space 2}-.0221093{col 29}{space 2} .0174768{col 40}{space 1}   -1.27{col 49}{space 3}0.206{col 57}{space 4}-.0563633{col 70}{space 3} .0121446
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2} .2933834{col 29}{space 2} .2952929{col 40}{space 1}    0.99{col 49}{space 3}0.320{col 57}{space 4}-.2853801{col 70}{space 3} .8721469
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2} .0604481{col 29}{space 2} .0397043{col 40}{space 1}    1.52{col 49}{space 3}0.128{col 57}{space 4}-.0173709{col 70}{space 3} .1382672
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.648356{col 29}{space 2} 2.612165{col 40}{space 1}   -0.63{col 49}{space 3}0.528{col 57}{space 4}-6.768106{col 70}{space 3} 3.471393
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}code           {col 17}{txt}{c |}
{space 1}var(university){c |}{col 17}{res}{space 2} .0309922{col 29}{space 2} .0195566{col 57}{space 4} .0089977{col 70}{space 3} .1067518
{txt}{space 6}var(_cons){c |}{col 17}{res}{space 2} 1.462251{col 29}{space 2} .4453748{col 57}{space 4} .8049368{col 70}{space 3} 2.656332
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 5384.61{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. // Model 3: horse race test (with FOTP)
. eststo FRE3: mixed diff_dem_vdem elec_frefair new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68636.775}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68636.775}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}12{txt}){col 67}={col 70}{res}   557.27
{txt}Log pseudolikelihood = {res}-68636.775{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}elec_frefair {c |}{col 15}{res}{space 2}-.0031379{col 27}{space 2} .0172375{col 38}{space 1}   -0.18{col 47}{space 3}0.856{col 55}{space 4}-.0369227{col 68}{space 3} .0306469
{txt}{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0749568{col 27}{space 2} .0386158{col 38}{space 1}   -1.94{col 47}{space 3}0.052{col 55}{space 4}-.1506424{col 68}{space 3} .0007288
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3990995{col 27}{space 2} .0584635{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5136858{col 68}{space 3}-.2845132
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1631805{col 27}{space 2} .0545845{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0561969{col 68}{space 3} .2701641
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.021554{col 27}{space 2} .0044224{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4}-.0302216{col 68}{space 3}-.0128863
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261872{col 27}{space 2} .0052115{col 38}{space 1}    5.02{col 47}{space 3}0.000{col 55}{space 4} .0159728{col 68}{space 3} .0364016
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0299351{col 27}{space 2} .0593635{col 38}{space 1}    0.50{col 47}{space 3}0.614{col 55}{space 4}-.0864152{col 68}{space 3} .1462854
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1383594{col 27}{space 2} .0860367{col 38}{space 1}   -1.61{col 47}{space 3}0.108{col 55}{space 4}-.3069882{col 68}{space 3} .0302694
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1630452{col 27}{space 2}  .030932{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1024197{col 68}{space 3} .2236708
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0103945{col 27}{space 2} .0380617{col 38}{space 1}    0.27{col 47}{space 3}0.785{col 55}{space 4}-.0642051{col 68}{space 3}  .084994
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2} .1372245{col 27}{space 2} .3672422{col 38}{space 1}    0.37{col 47}{space 3}0.709{col 55}{space 4}-.5825569{col 68}{space 3} .8570059
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0442957{col 27}{space 2} .0322056{col 38}{space 1}    1.38{col 47}{space 3}0.169{col 55}{space 4}-.0188261{col 68}{space 3} .1074175
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.239737{col 27}{space 2}  3.67087{col 38}{space 1}    0.61{col 47}{space 3}0.542{col 55}{space 4}-4.955035{col 68}{space 3} 9.434509
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0489344{col 44} .0289055{col 58} .0153749{col 70} .1557462
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.652701{col 44}  .388571{col 58} 1.042476{col 70} 2.620129
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315281{col 44} .4072908{col 58} 4.574059{col 70} 6.176618
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Model 4: horse race test (with MSF)
. eststo FRE4: mixed diff_dem_vdem elec_frefair new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68634.662}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68634.662}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}12{txt}){col 67}={col 70}{res}   436.45
{txt}Log pseudolikelihood = {res}-68634.662{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}elec_frefair {c |}{col 15}{res}{space 2}-.0035491{col 27}{space 2} .0143617{col 38}{space 1}   -0.25{col 47}{space 3}0.805{col 55}{space 4}-.0316975{col 68}{space 3} .0245992
{txt}{space 6}new_msf {c |}{col 15}{res}{space 2} -.068461{col 27}{space 2} .0215878{col 38}{space 1}   -3.17{col 47}{space 3}0.002{col 55}{space 4}-.1107724{col 68}{space 3}-.0261497
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3994283{col 27}{space 2} .0584838{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5140544{col 68}{space 3}-.2848022
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1630513{col 27}{space 2} .0545839{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0560688{col 68}{space 3} .2700338
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.021555{col 27}{space 2} .0044252{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4}-.0302282{col 68}{space 3}-.0128819
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0261835{col 27}{space 2} .0052131{col 38}{space 1}    5.02{col 47}{space 3}0.000{col 55}{space 4}  .015966{col 68}{space 3}  .036401
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0299579{col 27}{space 2} .0593433{col 38}{space 1}    0.50{col 47}{space 3}0.614{col 55}{space 4}-.0863528{col 68}{space 3} .1462685
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1386268{col 27}{space 2} .0860521{col 38}{space 1}   -1.61{col 47}{space 3}0.107{col 55}{space 4}-.3072859{col 68}{space 3} .0300322
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1631314{col 27}{space 2} .0309171{col 38}{space 1}    5.28{col 47}{space 3}0.000{col 55}{space 4}  .102535{col 68}{space 3} .2237279
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0104569{col 27}{space 2} .0380656{col 38}{space 1}    0.27{col 47}{space 3}0.784{col 55}{space 4}-.0641503{col 68}{space 3} .0850641
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.2050176{col 27}{space 2}  .387106{col 38}{space 1}   -0.53{col 47}{space 3}0.596{col 55}{space 4}-.9637314{col 68}{space 3} .5536963
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0077836{col 27}{space 2} .0352467{col 38}{space 1}    0.22{col 47}{space 3}0.825{col 55}{space 4}-.0612986{col 68}{space 3} .0768658
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.022597{col 27}{space 2} 3.899555{col 38}{space 1}    1.54{col 47}{space 3}0.122{col 55}{space 4}-1.620391{col 68}{space 3} 13.66558
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0492169{col 44} .0291037{col 58} .0154443{col 70} .1568413
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.362958{col 44} .3031223{col 58} .8814038{col 70} 2.107608
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315269{col 44} .4072889{col 58}  4.57405{col 70} 6.176602
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** References in "rob-check_summary.xlsx": rob_check >= 1 & rob_check <= 6
. /* Conduct placebo and horse race tests with elected officials index (v2x_elecoff) */
. // Rescale v2x_elecoff so that it ranges from 0 to 100
. gen elec_official = v2x_elecoff * 100
{txt}
{com}. 
. // Model 1: placebo test (without media freedom)
. eststo ELE1: mixed diff_dem_vdem elec_official university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68638.718}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68638.718}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}11{txt}){col 67}={col 70}{res}   283.22
{txt}Log pseudolikelihood = {res}-68638.718{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
elec_official {c |}{col 15}{res}{space 2}-.0119843{col 27}{space 2} .0083143{col 38}{space 1}   -1.44{col 47}{space 3}0.149{col 55}{space 4}-.0282801{col 68}{space 3} .0043114
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3988662{col 27}{space 2}  .058456{col 38}{space 1}   -6.82{col 47}{space 3}0.000{col 55}{space 4}-.5134379{col 68}{space 3}-.2842945
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1632305{col 27}{space 2}  .054592{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0562323{col 68}{space 3} .2702288
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215879{col 27}{space 2} .0044282{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4} -.030267{col 68}{space 3}-.0129088
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0262264{col 27}{space 2} .0052162{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0160028{col 68}{space 3} .0364499
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0299094{col 27}{space 2} .0593239{col 38}{space 1}    0.50{col 47}{space 3}0.614{col 55}{space 4}-.0863632{col 68}{space 3} .1461821
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1379614{col 27}{space 2} .0861279{col 38}{space 1}   -1.60{col 47}{space 3}0.109{col 55}{space 4} -.306769{col 68}{space 3} .0308463
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1630403{col 27}{space 2}  .030908{col 38}{space 1}    5.28{col 47}{space 3}0.000{col 55}{space 4} .1024618{col 68}{space 3} .2236187
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0106151{col 27}{space 2} .0380219{col 38}{space 1}    0.28{col 47}{space 3}0.780{col 55}{space 4}-.0639066{col 68}{space 3} .0851367
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2} .2368292{col 27}{space 2} .3676136{col 38}{space 1}    0.64{col 47}{space 3}0.519{col 55}{space 4}-.4836802{col 68}{space 3} .9573385
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0572826{col 27}{space 2} .0247793{col 38}{space 1}    2.31{col 47}{space 3}0.021{col 55}{space 4} .0087161{col 68}{space 3} .1058492
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.0446729{col 27}{space 2} 3.532786{col 38}{space 1}   -0.01{col 47}{space 3}0.990{col 55}{space 4}-6.968806{col 68}{space 3}  6.87946
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0489167{col 44} .0288754{col 58} .0153813{col 70} .1555678
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.972921{col 44}  .449214{col 58} 1.262699{col 70} 3.082617
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315282{col 44} .4072944{col 58} 4.574054{col 70} 6.176627
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Model 2: placebo test (but with binary DV)
. eststo FRE2: melogit overest_vdem elec_official university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-18064.559}  
Iteration 1:{space 3}log likelihood = {res:-18052.956}  
Iteration 2:{space 3}log likelihood = {res:-18052.953}  
Iteration 3:{space 3}log likelihood = {res:-18052.953}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-15571.324}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-15571.324}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-15559.698}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-15548.043}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-15543.479}  
Iteration 4:{space 3}log likelihood = {res: -15462.13}  
Iteration 5:{space 3}log likelihood = {res:-15461.826}  
Iteration 6:{space 3}log likelihood = {res:-15461.819}  
Iteration 7:{space 3}log likelihood = {res:-15461.819}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={res}{col 69}        22

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}       854
{col 63}{txt}avg{col 67}={res}{col 69}   1,382.5
{col 63}{txt}max{col 67}={res}{col 69}     1,997

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   462.84
{txt}Log likelihood = {res}-15461.819{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   overest_vdem{col 17}{c |} Coefficient{col 29}  Std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}elec_official {c |}{col 17}{res}{space 2}-.0085837{col 29}{space 2}  .006651{col 40}{space 1}   -1.29{col 49}{space 3}0.197{col 57}{space 4}-.0216195{col 70}{space 3} .0044521
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3300793{col 29}{space 2} .0582202{col 40}{space 1}   -5.67{col 49}{space 3}0.000{col 57}{space 4}-.4441887{col 70}{space 3}-.2159699
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1483491{col 29}{space 2} .0283008{col 40}{space 1}    5.24{col 49}{space 3}0.000{col 57}{space 4} .0928806{col 70}{space 3} .2038176
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0166719{col 29}{space 2}  .005191{col 40}{space 1}   -3.21{col 49}{space 3}0.001{col 57}{space 4} -.026846{col 70}{space 3}-.0064978
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0204299{col 29}{space 2}  .005622{col 40}{space 1}    3.63{col 49}{space 3}0.000{col 57}{space 4} .0094109{col 70}{space 3} .0314489
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0312224{col 29}{space 2} .0326714{col 40}{space 1}    0.96{col 49}{space 3}0.339{col 57}{space 4}-.0328122{col 70}{space 3} .0952571
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.0955313{col 29}{space 2} .0522783{col 40}{space 1}   -1.83{col 49}{space 3}0.068{col 57}{space 4}-.1979949{col 70}{space 3} .0069323
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1537844{col 29}{space 2}  .008439{col 40}{space 1}   18.22{col 49}{space 3}0.000{col 57}{space 4} .1372442{col 70}{space 3} .1703245
{txt}{space 3}social_class {c |}{col 17}{res}{space 2}-.0219783{col 29}{space 2} .0174766{col 40}{space 1}   -1.26{col 49}{space 3}0.209{col 57}{space 4}-.0562317{col 70}{space 3} .0122752
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2} .1716816{col 29}{space 2} .2758272{col 40}{space 1}    0.62{col 49}{space 3}0.534{col 57}{space 4}-.3689299{col 70}{space 3} .7122931
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2} .0492014{col 29}{space 2} .0386553{col 40}{space 1}    1.27{col 49}{space 3}0.203{col 57}{space 4}-.0265616{col 70}{space 3} .1249644
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.3300226{col 29}{space 2} 2.619479{col 40}{space 1}   -0.13{col 49}{space 3}0.900{col 57}{space 4}-5.464108{col 70}{space 3} 4.804062
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}code           {col 17}{txt}{c |}
{space 1}var(university){c |}{col 17}{res}{space 2} .0308935{col 29}{space 2}  .019538{col 57}{space 4} .0089442{col 70}{space 3} .1067069
{txt}{space 6}var(_cons){c |}{col 17}{res}{space 2} 1.434179{col 29}{space 2}  .436919{col 57}{space 4} .7893819{col 70}{space 3} 2.605672
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 5182.27{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. // Model 3: horse race test (with FOTP)
. eststo ELE3: mixed diff_dem_vdem elec_official new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -68634.93}  
Iteration 1:{space 3}log pseudolikelihood = {res: -68634.93}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}12{txt}){col 67}={col 70}{res}   478.49
{txt}Log pseudolikelihood = {res} -68634.93{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
elec_official {c |}{col 15}{res}{space 2}-.0132297{col 27}{space 2} .0071349{col 38}{space 1}   -1.85{col 47}{space 3}0.064{col 55}{space 4}-.0272139{col 68}{space 3} .0007545
{txt}{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0817269{col 27}{space 2} .0284023{col 38}{space 1}   -2.88{col 47}{space 3}0.004{col 55}{space 4}-.1373944{col 68}{space 3}-.0260593
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3991897{col 27}{space 2} .0585004{col 38}{space 1}   -6.82{col 47}{space 3}0.000{col 55}{space 4}-.5138484{col 68}{space 3} -.284531
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1632274{col 27}{space 2} .0545809{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0562509{col 68}{space 3}  .270204
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215797{col 27}{space 2} .0044289{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4}-.0302601{col 68}{space 3}-.0128993
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0262136{col 27}{space 2} .0052147{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4}  .015993{col 68}{space 3} .0364341
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0297927{col 27}{space 2} .0593658{col 38}{space 1}    0.50{col 47}{space 3}0.616{col 55}{space 4}-.0865621{col 68}{space 3} .1461474
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1377798{col 27}{space 2} .0861428{col 38}{space 1}   -1.60{col 47}{space 3}0.110{col 55}{space 4}-.3066167{col 68}{space 3}  .031057
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1631478{col 27}{space 2} .0309214{col 38}{space 1}    5.28{col 47}{space 3}0.000{col 55}{space 4} .1025429{col 68}{space 3} .2237526
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0104513{col 27}{space 2} .0380259{col 38}{space 1}    0.27{col 47}{space 3}0.783{col 55}{space 4}-.0640782{col 68}{space 3} .0849807
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2} .0828902{col 27}{space 2} .2897048{col 38}{space 1}    0.29{col 47}{space 3}0.775{col 55}{space 4}-.4849207{col 68}{space 3} .6507012
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0365553{col 27}{space 2} .0205746{col 38}{space 1}    1.78{col 47}{space 3}0.076{col 55}{space 4}-.0037702{col 68}{space 3} .0768808
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  3.89446{col 27}{space 2} 3.217213{col 38}{space 1}    1.21{col 47}{space 3}0.226{col 55}{space 4}-2.411161{col 68}{space 3} 10.20008
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0492688{col 44} .0290065{col 58} .0155394{col 70}   .15621
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.396588{col 44} .3007887{col 58} .9156758{col 70} 2.130075
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315267{col 44} .4072892{col 58} 4.574047{col 70}   6.1766
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Model 4: horse race test (with MSF)
. eststo ELE4: mixed diff_dem_vdem elec_official new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68633.196}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68633.196}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}12{txt}){col 67}={col 70}{res}   419.57
{txt}Log pseudolikelihood = {res}-68633.196{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
elec_official {c |}{col 15}{res}{space 2}-.0108537{col 27}{space 2} .0060207{col 38}{space 1}   -1.80{col 47}{space 3}0.071{col 55}{space 4} -.022654{col 68}{space 3} .0009466
{txt}{space 6}new_msf {c |}{col 15}{res}{space 2}-.0689852{col 27}{space 2}  .017217{col 38}{space 1}   -4.01{col 47}{space 3}0.000{col 55}{space 4}-.1027298{col 68}{space 3}-.0352406
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3995249{col 27}{space 2} .0585131{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5142084{col 68}{space 3}-.2848414
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1630809{col 27}{space 2} .0545844{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0560973{col 68}{space 3} .2700645
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215821{col 27}{space 2} .0044314{col 38}{space 1}   -4.87{col 47}{space 3}0.000{col 55}{space 4}-.0302676{col 68}{space 3}-.0128967
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0262112{col 27}{space 2}  .005216{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4}  .015988{col 68}{space 3} .0364345
{txt}{space 6}married {c |}{col 15}{res}{space 2}  .029829{col 27}{space 2} .0593571{col 38}{space 1}    0.50{col 47}{space 3}0.615{col 55}{space 4}-.0865088{col 68}{space 3} .1461669
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} -.138088{col 27}{space 2} .0861019{col 38}{space 1}   -1.60{col 47}{space 3}0.109{col 55}{space 4}-.3068447{col 68}{space 3} .0306686
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1632331{col 27}{space 2} .0309052{col 38}{space 1}    5.28{col 47}{space 3}0.000{col 55}{space 4}   .10266{col 68}{space 3} .2238062
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0105455{col 27}{space 2}  .038027{col 38}{space 1}    0.28{col 47}{space 3}0.782{col 55}{space 4} -.063986{col 68}{space 3} .0850771
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.2497096{col 27}{space 2} .2924076{col 38}{space 1}   -0.85{col 47}{space 3}0.393{col 55}{space 4}-.8228179{col 68}{space 3} .3233987
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0018685{col 27}{space 2} .0237761{col 38}{space 1}    0.08{col 47}{space 3}0.937{col 55}{space 4}-.0447318{col 68}{space 3} .0484687
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.208335{col 27}{space 2} 3.152571{col 38}{space 1}    2.29{col 47}{space 3}0.022{col 55}{space 4}  1.02941{col 68}{space 3} 13.38726
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0495522{col 44} .0292294{col 58} .0155942{col 70} .1574573
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.192227{col 44} .2209333{col 58} .8291277{col 70} 1.714337
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315255{col 44}  .407288{col 58} 4.574037{col 70} 6.176585
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 7
. /* Drop individuals who believe democracy is unimportant (threshold = 6) */
. eststo UNIM1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V140 > 5 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-58256.771}  
Iteration 1:{space 3}log pseudolikelihood = {res:-58256.771}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    25,871
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       743
{txt}{col 63}avg{col 67}={col 69}{res}   1,176.0
{txt}{col 63}max{col 67}={col 69}{res}     1,692
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   306.31
{txt}Log pseudolikelihood = {res}-58256.771{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1067204{col 27}{space 2} .0262718{col 38}{space 1}   -4.06{col 47}{space 3}0.000{col 55}{space 4}-.1582121{col 68}{space 3}-.0552286
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4314686{col 27}{space 2} .0686705{col 38}{space 1}   -6.28{col 47}{space 3}0.000{col 55}{space 4}-.5660604{col 68}{space 3}-.2968769
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1781534{col 27}{space 2} .0519432{col 38}{space 1}    3.43{col 47}{space 3}0.001{col 55}{space 4} .0763465{col 68}{space 3} .2799603
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0251059{col 27}{space 2} .0057385{col 38}{space 1}   -4.37{col 47}{space 3}0.000{col 55}{space 4}-.0363532{col 68}{space 3}-.0138586
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0305096{col 27}{space 2} .0067319{col 38}{space 1}    4.53{col 47}{space 3}0.000{col 55}{space 4} .0173153{col 68}{space 3}  .043704
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0203217{col 27}{space 2} .0580113{col 38}{space 1}    0.35{col 47}{space 3}0.726{col 55}{space 4}-.0933784{col 68}{space 3} .1340217
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1654662{col 27}{space 2} .1086282{col 38}{space 1}   -1.52{col 47}{space 3}0.128{col 55}{space 4}-.3783736{col 68}{space 3} .0474412
{txt}{space 7}income {c |}{col 15}{res}{space 2}  .161081{col 27}{space 2} .0327895{col 38}{space 1}    4.91{col 47}{space 3}0.000{col 55}{space 4} .0968147{col 68}{space 3} .2253474
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0029442{col 27}{space 2} .0365251{col 38}{space 1}    0.08{col 47}{space 3}0.936{col 55}{space 4}-.0686436{col 68}{space 3}  .074532
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6437243{col 27}{space 2} .2594811{col 38}{space 1}   -2.48{col 47}{space 3}0.013{col 55}{space 4}-1.152298{col 68}{space 3}-.1351508
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0159916{col 27}{space 2} .0232461{col 38}{space 1}   -0.69{col 47}{space 3}0.492{col 55}{space 4}-.0615532{col 68}{space 3}   .02957
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0187935{col 27}{space 2} .0416943{col 38}{space 1}    0.45{col 47}{space 3}0.652{col 55}{space 4}-.0629258{col 68}{space 3} .1005128
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0474648{col 27}{space 2} .0328357{col 38}{space 1}    1.45{col 47}{space 3}0.148{col 55}{space 4} -.016892{col 68}{space 3} .1118216
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.906249{col 27}{space 2} 2.101203{col 38}{space 1}    3.76{col 47}{space 3}0.000{col 55}{space 4} 3.787967{col 68}{space 3} 12.02453
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0772691{col 44} .0447361{col 58} .0248422{col 70} .2403374
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.082721{col 44} .2144263{col 58} .7344148{col 70} 1.596217
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.260121{col 44} .4441446{col 58}  4.45783{col 70} 6.206803
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo UNIM2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V140 > 5 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-58256.449}  
Iteration 1:{space 3}log pseudolikelihood = {res:-58256.449}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    25,871
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       743
{txt}{col 63}avg{col 67}={col 69}{res}   1,176.0
{txt}{col 63}max{col 67}={col 69}{res}     1,692
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   309.49
{txt}Log pseudolikelihood = {res}-58256.449{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0710927{col 27}{space 2} .0126366{col 38}{space 1}   -5.63{col 47}{space 3}0.000{col 55}{space 4}  -.09586{col 68}{space 3}-.0463254
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4318508{col 27}{space 2} .0686838{col 38}{space 1}   -6.29{col 47}{space 3}0.000{col 55}{space 4}-.5664685{col 68}{space 3}-.2972331
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1780367{col 27}{space 2} .0519214{col 38}{space 1}    3.43{col 47}{space 3}0.001{col 55}{space 4} .0762725{col 68}{space 3} .2798008
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0251211{col 27}{space 2} .0057409{col 38}{space 1}   -4.38{col 47}{space 3}0.000{col 55}{space 4} -.036373{col 68}{space 3}-.0138692
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0305212{col 27}{space 2} .0067319{col 38}{space 1}    4.53{col 47}{space 3}0.000{col 55}{space 4} .0173269{col 68}{space 3} .0437155
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0200556{col 27}{space 2} .0580827{col 38}{space 1}    0.35{col 47}{space 3}0.730{col 55}{space 4}-.0937845{col 68}{space 3} .1338957
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1660343{col 27}{space 2} .1085551{col 38}{space 1}   -1.53{col 47}{space 3}0.126{col 55}{space 4}-.3787985{col 68}{space 3} .0467298
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1611611{col 27}{space 2} .0327546{col 38}{space 1}    4.92{col 47}{space 3}0.000{col 55}{space 4} .0969633{col 68}{space 3} .2253589
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0028721{col 27}{space 2} .0365062{col 38}{space 1}    0.08{col 47}{space 3}0.937{col 55}{space 4}-.0686787{col 68}{space 3} .0744229
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.8373611{col 27}{space 2} .2986025{col 38}{space 1}   -2.80{col 47}{space 3}0.005{col 55}{space 4}-1.422611{col 68}{space 3}-.2521109
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0505258{col 27}{space 2} .0280083{col 38}{space 1}   -1.80{col 47}{space 3}0.071{col 55}{space 4}-.1054211{col 68}{space 3} .0043694
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0336301{col 27}{space 2} .0390744{col 38}{space 1}   -0.86{col 47}{space 3}0.389{col 55}{space 4}-.1102146{col 68}{space 3} .0429543
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0742919{col 27}{space 2} .0316423{col 38}{space 1}    2.35{col 47}{space 3}0.019{col 55}{space 4} .0122742{col 68}{space 3} .1363096
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 10.41017{col 27}{space 2} 2.572722{col 38}{space 1}    4.05{col 47}{space 3}0.000{col 55}{space 4} 5.367728{col 68}{space 3} 15.45261
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0778567{col 44} .0451293{col 58} .0249977{col 70} .2424888
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.051465{col 44} .3112706{col 58} .5885853{col 70} 1.878367
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.260099{col 44} .4441417{col 58} 4.457813{col 70} 6.206774
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 8
. /* Drop individuals who believe democracy is unimportant (threshold = 10) */
. eststo UNIM3: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V140 == 10 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-27328.254}  
Iteration 1:{space 3}log pseudolikelihood = {res:-27328.253}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    11,464
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       287
{txt}{col 63}avg{col 67}={col 69}{res}     521.1
{txt}{col 63}max{col 67}={col 69}{res}     1,041
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   217.52
{txt}Log pseudolikelihood = {res}-27328.253{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1199541{col 27}{space 2} .0292654{col 38}{space 1}   -4.10{col 47}{space 3}0.000{col 55}{space 4}-.1773133{col 68}{space 3} -.062595
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4425343{col 27}{space 2} .0730437{col 38}{space 1}   -6.06{col 47}{space 3}0.000{col 55}{space 4}-.5856972{col 68}{space 3}-.2993713
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1535187{col 27}{space 2} .0849849{col 38}{space 1}    1.81{col 47}{space 3}0.071{col 55}{space 4}-.0130486{col 68}{space 3} .3200861
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0426629{col 27}{space 2} .0098739{col 38}{space 1}   -4.32{col 47}{space 3}0.000{col 55}{space 4}-.0620154{col 68}{space 3}-.0233104
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2}   .05019{col 27}{space 2} .0115717{col 38}{space 1}    4.34{col 47}{space 3}0.000{col 55}{space 4} .0275099{col 68}{space 3} .0728701
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0531356{col 27}{space 2} .0703592{col 38}{space 1}    0.76{col 47}{space 3}0.450{col 55}{space 4}-.0847659{col 68}{space 3} .1910371
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1358984{col 27}{space 2}  .112422{col 38}{space 1}   -1.21{col 47}{space 3}0.227{col 55}{space 4}-.3562414{col 68}{space 3} .0844446
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1735477{col 27}{space 2} .0382211{col 38}{space 1}    4.54{col 47}{space 3}0.000{col 55}{space 4} .0986358{col 68}{space 3} .2484596
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} -.006859{col 27}{space 2} .0447886{col 38}{space 1}   -0.15{col 47}{space 3}0.878{col 55}{space 4}-.0946431{col 68}{space 3}  .080925
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7984766{col 27}{space 2} .3043678{col 38}{space 1}   -2.62{col 47}{space 3}0.009{col 55}{space 4}-1.395027{col 68}{space 3}-.2019267
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0261682{col 27}{space 2} .0260153{col 38}{space 1}   -1.01{col 47}{space 3}0.314{col 55}{space 4}-.0771573{col 68}{space 3} .0248209
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0121963{col 27}{space 2} .0439166{col 38}{space 1}    0.28{col 47}{space 3}0.781{col 55}{space 4}-.0738788{col 68}{space 3} .0982713
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0687984{col 27}{space 2} .0356562{col 38}{space 1}    1.93{col 47}{space 3}0.054{col 55}{space 4}-.0010863{col 68}{space 3} .1386832
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.429587{col 27}{space 2} 2.465115{col 38}{space 1}    3.83{col 47}{space 3}0.000{col 55}{space 4} 4.598051{col 68}{space 3} 14.26112
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0572466{col 44} .0567536{col 58} .0082012{col 70} .3995956
{txt}{space 18}var(_cons) {c |}{res}{col 33}  1.38337{col 44} .2958983{col 58} .9096393{col 70} 2.103815
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 6.821259{col 44} .5361431{col 58} 5.847377{col 70} 7.957341
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo UNIM4: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V140 == 10 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-27328.332}  
Iteration 1:{space 3}log pseudolikelihood = {res: -27328.33}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    11,464
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       287
{txt}{col 63}avg{col 67}={col 69}{res}     521.1
{txt}{col 63}max{col 67}={col 69}{res}     1,041
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   180.95
{txt}Log pseudolikelihood = {res} -27328.33{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0780034{col 27}{space 2}  .014876{col 38}{space 1}   -5.24{col 47}{space 3}0.000{col 55}{space 4}-.1071598{col 68}{space 3} -.048847
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4428875{col 27}{space 2} .0730869{col 38}{space 1}   -6.06{col 47}{space 3}0.000{col 55}{space 4}-.5861352{col 68}{space 3}-.2996398
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1533116{col 27}{space 2} .0849894{col 38}{space 1}    1.80{col 47}{space 3}0.071{col 55}{space 4}-.0132646{col 68}{space 3} .3198877
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0426717{col 27}{space 2} .0098759{col 38}{space 1}   -4.32{col 47}{space 3}0.000{col 55}{space 4} -.062028{col 68}{space 3}-.0233153
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0501893{col 27}{space 2} .0115691{col 38}{space 1}    4.34{col 47}{space 3}0.000{col 55}{space 4} .0275143{col 68}{space 3} .0728643
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0524708{col 27}{space 2} .0703229{col 38}{space 1}    0.75{col 47}{space 3}0.456{col 55}{space 4}-.0853597{col 68}{space 3} .1903012
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1372599{col 27}{space 2} .1122078{col 38}{space 1}   -1.22{col 47}{space 3}0.221{col 55}{space 4}-.3571831{col 68}{space 3} .0826633
{txt}{space 7}income {c |}{col 15}{res}{space 2}  .173725{col 27}{space 2} .0381458{col 38}{space 1}    4.55{col 47}{space 3}0.000{col 55}{space 4} .0989606{col 68}{space 3} .2484894
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0071342{col 27}{space 2} .0447427{col 38}{space 1}   -0.16{col 47}{space 3}0.873{col 55}{space 4}-.0948284{col 68}{space 3}   .08056
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-1.002692{col 27}{space 2}  .367587{col 38}{space 1}   -2.73{col 47}{space 3}0.006{col 55}{space 4} -1.72315{col 68}{space 3}-.2822352
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0636046{col 27}{space 2} .0338782{col 38}{space 1}   -1.88{col 47}{space 3}0.060{col 55}{space 4}-.1300047{col 68}{space 3} .0027954
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0471037{col 27}{space 2} .0424085{col 38}{space 1}   -1.11{col 47}{space 3}0.267{col 55}{space 4}-.1302227{col 68}{space 3} .0360154
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0992143{col 27}{space 2} .0364012{col 38}{space 1}    2.73{col 47}{space 3}0.006{col 55}{space 4} .0278693{col 68}{space 3} .1705593
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  12.0454{col 27}{space 2} 3.212136{col 38}{space 1}    3.75{col 47}{space 3}0.000{col 55}{space 4} 5.749733{col 68}{space 3} 18.34108
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0576587{col 44} .0573281{col 58} .0082138{col 70} .4047484
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.394721{col 44} .4064495{col 58} .7878255{col 70} 2.469132
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 6.821212{col 44} .5361618{col 58} 5.847299{col 70} 7.957338
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** References in "rob-check_summary.xlsx": rob_check >= 9 & rob_check <= 14
. /* Use other democracy indices */
. // Use vdem_libdem
. eststo OTH1: mixed diff_dem_libdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68631.044}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68631.044}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   289.15
{txt}Log pseudolikelihood = {res}-68631.044{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_libdem{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}new_fotp {c |}{col 17}{res}{space 2}-.0884116{col 29}{space 2} .0294901{col 40}{space 1}   -3.00{col 49}{space 3}0.003{col 57}{space 4}-.1462112{col 70}{space 3}-.0306121
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3990003{col 29}{space 2} .0584924{col 40}{space 1}   -6.82{col 49}{space 3}0.000{col 57}{space 4}-.5136434{col 70}{space 3}-.2843573
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1631899{col 29}{space 2} .0545602{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0562538{col 70}{space 3}  .270126
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0215492{col 29}{space 2} .0044198{col 40}{space 1}   -4.88{col 49}{space 3}0.000{col 57}{space 4}-.0302118{col 70}{space 3}-.0128866
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0261712{col 29}{space 2} .0052041{col 40}{space 1}    5.03{col 49}{space 3}0.000{col 57}{space 4} .0159714{col 70}{space 3} .0363709
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0298268{col 29}{space 2} .0593305{col 40}{space 1}    0.50{col 49}{space 3}0.615{col 57}{space 4}-.0864589{col 70}{space 3} .1461125
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.1381585{col 29}{space 2} .0860347{col 40}{space 1}   -1.61{col 49}{space 3}0.108{col 57}{space 4}-.3067834{col 70}{space 3} .0304664
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1628944{col 29}{space 2} .0309411{col 40}{space 1}    5.26{col 49}{space 3}0.000{col 57}{space 4}  .102251{col 70}{space 3} .2235378
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0104166{col 29}{space 2} .0380357{col 40}{space 1}    0.27{col 49}{space 3}0.784{col 57}{space 4} -.064132{col 70}{space 3} .0849651
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.3254776{col 29}{space 2} .2609004{col 40}{space 1}   -1.25{col 49}{space 3}0.212{col 57}{space 4}-.8368329{col 70}{space 3} .1858778
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0016046{col 29}{space 2} .0279758{col 40}{space 1}   -0.06{col 49}{space 3}0.954{col 57}{space 4}-.0564362{col 70}{space 3}  .053227
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2} .0270248{col 29}{space 2} .0358621{col 40}{space 1}    0.75{col 49}{space 3}0.451{col 57}{space 4}-.0432636{col 70}{space 3} .0973132
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2} .0197861{col 29}{space 2} .0316124{col 40}{space 1}    0.63{col 49}{space 3}0.531{col 57}{space 4} -.042173{col 70}{space 3} .0817452
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 6.212194{col 29}{space 2} 2.301705{col 40}{space 1}    2.70{col 49}{space 3}0.007{col 57}{space 4} 1.700935{col 70}{space 3} 10.72345
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0489766{col 44} .0286592{col 58}  .015556{col 70} .1541978
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9794502{col 44} .2328599{col 58} .6146289{col 70} 1.560816
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315281{col 44} .4072829{col 58} 4.574072{col 70} 6.176599
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo OTH2: mixed diff_dem_libdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68629.093}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68629.093}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   278.75
{txt}Log pseudolikelihood = {res}-68629.093{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_libdem{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}new_msf {c |}{col 17}{res}{space 2}-.0656964{col 29}{space 2}  .014482{col 40}{space 1}   -4.54{col 49}{space 3}0.000{col 57}{space 4}-.0940806{col 70}{space 3}-.0373122
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3994848{col 29}{space 2} .0585183{col 40}{space 1}   -6.83{col 49}{space 3}0.000{col 57}{space 4}-.5141787{col 70}{space 3}-.2847909
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1630362{col 29}{space 2} .0545369{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0561458{col 70}{space 3} .2699265
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0215534{col 29}{space 2} .0044226{col 40}{space 1}   -4.87{col 49}{space 3}0.000{col 57}{space 4}-.0302216{col 70}{space 3}-.0128852
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0261663{col 29}{space 2} .0052036{col 40}{space 1}    5.03{col 49}{space 3}0.000{col 57}{space 4} .0159674{col 70}{space 3} .0363653
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0296253{col 29}{space 2} .0594009{col 40}{space 1}    0.50{col 49}{space 3}0.618{col 57}{space 4}-.0867984{col 70}{space 3}  .146049
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.1387313{col 29}{space 2} .0860443{col 40}{space 1}   -1.61{col 49}{space 3}0.107{col 57}{space 4} -.307375{col 70}{space 3} .0299124
{txt}{space 9}income {c |}{col 17}{res}{space 2}  .162981{col 29}{space 2}  .030914{col 40}{space 1}    5.27{col 49}{space 3}0.000{col 57}{space 4} .1023906{col 70}{space 3} .2235713
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0103169{col 29}{space 2}  .038002{col 40}{space 1}    0.27{col 49}{space 3}0.786{col 57}{space 4}-.0641657{col 70}{space 3} .0847995
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.5326161{col 29}{space 2} .2892681{col 40}{space 1}   -1.84{col 49}{space 3}0.066{col 57}{space 4}-1.099571{col 70}{space 3}  .034339
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0353214{col 29}{space 2} .0253948{col 40}{space 1}   -1.39{col 49}{space 3}0.164{col 57}{space 4}-.0850942{col 70}{space 3} .0144514
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2}-.0152226{col 29}{space 2} .0254659{col 40}{space 1}   -0.60{col 49}{space 3}0.550{col 57}{space 4}-.0651349{col 70}{space 3} .0346898
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2} .0410663{col 29}{space 2}  .025087{col 40}{space 1}    1.64{col 49}{space 3}0.102{col 57}{space 4}-.0081032{col 70}{space 3} .0902358
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 8.991254{col 29}{space 2} 2.593446{col 40}{space 1}    3.47{col 49}{space 3}0.001{col 57}{space 4} 3.908194{col 70}{space 3} 14.07432
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0493131{col 44} .0288712{col 58} .0156535{col 70} .1553504
{txt}{space 18}var(_cons) {c |}{res}{col 33} .8193181{col 44} .2478803{col 58} .4528205{col 70} 1.482446
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315266{col 44} .4072804{col 58} 4.574062{col 70}  6.17658
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Use vdem_partipdem
. eststo OTH3: mixed diff_dem_partipdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68631.166}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68631.166}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   303.06
{txt}Log pseudolikelihood = {res}-68631.166{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 84:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}diff_dem_partipdem{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}new_fotp {c |}{col 20}{res}{space 2} -.075091{col 32}{space 2} .0271645{col 43}{space 1}   -2.76{col 52}{space 3}0.006{col 60}{space 4}-.1283324{col 73}{space 3}-.0218497
{txt}{space 8}university {c |}{col 20}{res}{space 2}-.3988624{col 32}{space 2} .0584299{col 43}{space 1}   -6.83{col 52}{space 3}0.000{col 60}{space 4} -.513383{col 73}{space 3}-.2843419
{txt}{space 12}female {c |}{col 20}{res}{space 2} .1631618{col 32}{space 2} .0545695{col 43}{space 1}    2.99{col 52}{space 3}0.003{col 60}{space 4} .0562076{col 73}{space 3}  .270116
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0215619{col 32}{space 2} .0044163{col 43}{space 1}   -4.88{col 52}{space 3}0.000{col 60}{space 4}-.0302176{col 73}{space 3}-.0129062
{txt}{space 12}age_sq {c |}{col 20}{res}{space 2} .0261828{col 32}{space 2} .0052036{col 43}{space 1}    5.03{col 52}{space 3}0.000{col 60}{space 4} .0159839{col 73}{space 3} .0363816
{txt}{space 11}married {c |}{col 20}{res}{space 2} .0301646{col 32}{space 2} .0592739{col 43}{space 1}    0.51{col 52}{space 3}0.611{col 60}{space 4}-.0860101{col 73}{space 3} .1463394
{txt}{space 8}unemployed {c |}{col 20}{res}{space 2}-.1377228{col 32}{space 2}  .086047{col 43}{space 1}   -1.60{col 52}{space 3}0.109{col 60}{space 4}-.3063718{col 73}{space 3} .0309261
{txt}{space 12}income {c |}{col 20}{res}{space 2}   .16285{col 32}{space 2}  .030967{col 43}{space 1}    5.26{col 52}{space 3}0.000{col 60}{space 4} .1021558{col 73}{space 3} .2235441
{txt}{space 6}social_class {c |}{col 20}{res}{space 2} .0106704{col 32}{space 2} .0380506{col 43}{space 1}    0.28{col 52}{space 3}0.779{col 60}{space 4}-.0639074{col 73}{space 3} .0852482
{txt}{space 12}ln_gdp {c |}{col 20}{res}{space 2}-.1661426{col 32}{space 2} .2455473{col 43}{space 1}   -0.68{col 52}{space 3}0.499{col 60}{space 4}-.6474065{col 73}{space 3} .3151214
{txt}{space 5}growth_one_yr {c |}{col 20}{res}{space 2}-.0088853{col 32}{space 2} .0286179{col 43}{space 1}   -0.31{col 52}{space 3}0.756{col 60}{space 4}-.0649754{col 73}{space 3} .0472048
{txt}{space 11}new_rol {c |}{col 20}{res}{space 2} .0478628{col 32}{space 2} .0326509{col 43}{space 1}    1.47{col 52}{space 3}0.143{col 60}{space 4}-.0161317{col 73}{space 3} .1118573
{txt}{space 11}new_gov {c |}{col 20}{res}{space 2} .0173106{col 32}{space 2} .0282474{col 43}{space 1}    0.61{col 52}{space 3}0.540{col 60}{space 4}-.0380533{col 73}{space 3} .0726746
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 3.782905{col 32}{space 2} 1.963337{col 43}{space 1}    1.93{col 52}{space 3}0.054{col 60}{space 4}-.0651641{col 73}{space 3} 7.630974
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0484964{col 44} .0284457{col 58} .0153615{col 70} .1531031
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9902879{col 44}  .235699{col 58} .6211072{col 70} 1.578907
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315303{col 44} .4072901{col 58} 4.574081{col 70} 6.176638
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo OTH4: mixed diff_dem_partipdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68629.544}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68629.544}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   392.64
{txt}Log pseudolikelihood = {res}-68629.544{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 84:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}diff_dem_partipdem{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}new_msf {c |}{col 20}{res}{space 2}-.0569382{col 32}{space 2} .0136526{col 43}{space 1}   -4.17{col 52}{space 3}0.000{col 60}{space 4}-.0836968{col 73}{space 3}-.0301796
{txt}{space 8}university {c |}{col 20}{res}{space 2}-.3992502{col 32}{space 2} .0584449{col 43}{space 1}   -6.83{col 52}{space 3}0.000{col 60}{space 4}-.5138002{col 73}{space 3}-.2847002
{txt}{space 12}female {c |}{col 20}{res}{space 2} .1630301{col 32}{space 2} .0545485{col 43}{space 1}    2.99{col 52}{space 3}0.003{col 60}{space 4}  .056117{col 73}{space 3} .2699432
{txt}{space 15}age {c |}{col 20}{res}{space 2}-.0215674{col 32}{space 2} .0044182{col 43}{space 1}   -4.88{col 52}{space 3}0.000{col 60}{space 4}-.0302269{col 73}{space 3}-.0129079
{txt}{space 12}age_sq {c |}{col 20}{res}{space 2} .0261808{col 32}{space 2} .0052033{col 43}{space 1}    5.03{col 52}{space 3}0.000{col 60}{space 4} .0159826{col 73}{space 3}  .036379
{txt}{space 11}married {c |}{col 20}{res}{space 2} .0300457{col 32}{space 2} .0593185{col 43}{space 1}    0.51{col 52}{space 3}0.612{col 60}{space 4}-.0862164{col 73}{space 3} .1463079
{txt}{space 8}unemployed {c |}{col 20}{res}{space 2}-.1381226{col 32}{space 2} .0860472{col 43}{space 1}   -1.61{col 52}{space 3}0.108{col 60}{space 4} -.306772{col 73}{space 3} .0305267
{txt}{space 12}income {c |}{col 20}{res}{space 2} .1629137{col 32}{space 2} .0309499{col 43}{space 1}    5.26{col 52}{space 3}0.000{col 60}{space 4} .1022531{col 73}{space 3} .2235743
{txt}{space 6}social_class {c |}{col 20}{res}{space 2} .0106292{col 32}{space 2}  .038029{col 43}{space 1}    0.28{col 52}{space 3}0.780{col 60}{space 4}-.0639064{col 73}{space 3} .0851647
{txt}{space 12}ln_gdp {c |}{col 20}{res}{space 2}-.3498976{col 32}{space 2} .2626642{col 43}{space 1}   -1.33{col 52}{space 3}0.183{col 60}{space 4}  -.86471{col 73}{space 3} .1649148
{txt}{space 5}growth_one_yr {c |}{col 20}{res}{space 2}-.0383754{col 32}{space 2}  .025756{col 43}{space 1}   -1.49{col 52}{space 3}0.136{col 60}{space 4}-.0888561{col 73}{space 3} .0121054
{txt}{space 11}new_rol {c |}{col 20}{res}{space 2} .0121829{col 32}{space 2}  .025317{col 43}{space 1}    0.48{col 52}{space 3}0.630{col 60}{space 4}-.0374375{col 73}{space 3} .0618034
{txt}{space 11}new_gov {c |}{col 20}{res}{space 2} .0352235{col 32}{space 2} .0233761{col 43}{space 1}    1.51{col 52}{space 3}0.132{col 60}{space 4}-.0105928{col 73}{space 3} .0810399
{txt}{space 13}_cons {c |}{col 20}{res}{space 2} 6.261228{col 32}{space 2} 2.190281{col 43}{space 1}    2.86{col 52}{space 3}0.004{col 60}{space 4} 1.968357{col 73}{space 3}  10.5541
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0487134{col 44}   .02856{col 58} .0154384{col 70} .1537079
{txt}{space 18}var(_cons) {c |}{res}{col 33} .8537932{col 44} .2348609{col 58} .4979718{col 70} 1.463864
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315293{col 44} .4072895{col 58} 4.574073{col 70} 6.176627
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Use vdem_delibdem
. eststo OTH5: mixed diff_dem_delibdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68631.701}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68631.701}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   367.79
{txt}Log pseudolikelihood = {res}-68631.701{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}diff_dem_delibdem{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}new_fotp {c |}{col 19}{res}{space 2}  -.09577{col 31}{space 2} .0260188{col 42}{space 1}   -3.68{col 51}{space 3}0.000{col 59}{space 4}-.1467659{col 72}{space 3}-.0447742
{txt}{space 7}university {c |}{col 19}{res}{space 2}-.3990375{col 31}{space 2}  .058533{col 42}{space 1}   -6.82{col 51}{space 3}0.000{col 59}{space 4}-.5137602{col 72}{space 3}-.2843149
{txt}{space 11}female {c |}{col 19}{res}{space 2}   .16305{col 31}{space 2} .0545724{col 42}{space 1}    2.99{col 51}{space 3}0.003{col 59}{space 4}   .05609{col 72}{space 3} .2700099
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0215398{col 31}{space 2} .0044225{col 42}{space 1}   -4.87{col 51}{space 3}0.000{col 59}{space 4}-.0302078{col 72}{space 3}-.0128717
{txt}{space 11}age_sq {c |}{col 19}{res}{space 2} .0261647{col 31}{space 2} .0052073{col 42}{space 1}    5.02{col 51}{space 3}0.000{col 59}{space 4} .0159586{col 72}{space 3} .0363707
{txt}{space 10}married {c |}{col 19}{res}{space 2}  .029349{col 31}{space 2}   .05946{col 42}{space 1}    0.49{col 51}{space 3}0.622{col 59}{space 4}-.0871905{col 72}{space 3} .1458885
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1374712{col 31}{space 2} .0860855{col 42}{space 1}   -1.60{col 51}{space 3}0.110{col 59}{space 4}-.3061958{col 72}{space 3} .0312534
{txt}{space 11}income {c |}{col 19}{res}{space 2} .1629644{col 31}{space 2} .0309435{col 42}{space 1}    5.27{col 51}{space 3}0.000{col 59}{space 4} .1023162{col 72}{space 3} .2236125
{txt}{space 5}social_class {c |}{col 19}{res}{space 2} .0105761{col 31}{space 2} .0380964{col 42}{space 1}    0.28{col 51}{space 3}0.781{col 59}{space 4}-.0640915{col 72}{space 3} .0852437
{txt}{space 11}ln_gdp {c |}{col 19}{res}{space 2}-.3447206{col 31}{space 2} .3127252{col 42}{space 1}   -1.10{col 51}{space 3}0.270{col 59}{space 4}-.9576508{col 72}{space 3} .2682096
{txt}{space 4}growth_one_yr {c |}{col 19}{res}{space 2}-.0082355{col 31}{space 2} .0263802{col 42}{space 1}   -0.31{col 51}{space 3}0.755{col 59}{space 4}-.0599397{col 72}{space 3} .0434686
{txt}{space 10}new_rol {c |}{col 19}{res}{space 2} .0339439{col 31}{space 2} .0386001{col 42}{space 1}    0.88{col 51}{space 3}0.379{col 59}{space 4}-.0417108{col 72}{space 3} .1095986
{txt}{space 10}new_gov {c |}{col 19}{res}{space 2} .0184491{col 31}{space 2} .0363437{col 42}{space 1}    0.51{col 51}{space 3}0.612{col 59}{space 4}-.0527831{col 72}{space 3} .0896814
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  6.13467{col 31}{space 2} 2.561807{col 42}{space 1}    2.39{col 51}{space 3}0.017{col 59}{space 4} 1.113622{col 72}{space 3} 11.15572
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0492539{col 44} .0289529{col 58} .0155625{col 70}  .155884
{txt}{space 18}var(_cons) {c |}{res}{col 33}  1.03982{col 44} .2069347{col 58}   .70398{col 70} 1.535875
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315269{col 44} .4072821{col 58} 4.574061{col 70} 6.176586
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo OTH6: mixed diff_dem_delibdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68631.099}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68631.099}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   556.10
{txt}Log pseudolikelihood = {res}-68631.099{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}diff_dem_delibdem{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}new_msf {c |}{col 19}{res}{space 2}-.0652367{col 31}{space 2} .0140919{col 42}{space 1}   -4.63{col 51}{space 3}0.000{col 59}{space 4}-.0928563{col 72}{space 3} -.037617
{txt}{space 7}university {c |}{col 19}{res}{space 2}-.3993877{col 31}{space 2} .0585295{col 42}{space 1}   -6.82{col 51}{space 3}0.000{col 59}{space 4}-.5141034{col 72}{space 3}-.2846719
{txt}{space 11}female {c |}{col 19}{res}{space 2} .1629301{col 31}{space 2} .0545602{col 42}{space 1}    2.99{col 51}{space 3}0.003{col 59}{space 4}  .055994{col 72}{space 3} .2698662
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0215486{col 31}{space 2} .0044255{col 42}{space 1}   -4.87{col 51}{space 3}0.000{col 59}{space 4}-.0302224{col 72}{space 3}-.0128748
{txt}{space 11}age_sq {c |}{col 19}{res}{space 2} .0261692{col 31}{space 2} .0052083{col 42}{space 1}    5.02{col 51}{space 3}0.000{col 59}{space 4} .0159611{col 72}{space 3} .0363774
{txt}{space 10}married {c |}{col 19}{res}{space 2} .0291274{col 31}{space 2}  .059527{col 42}{space 1}    0.49{col 51}{space 3}0.625{col 59}{space 4}-.0875434{col 72}{space 3} .1457981
{txt}{space 7}unemployed {c |}{col 19}{res}{space 2}-.1379234{col 31}{space 2}  .086087{col 42}{space 1}   -1.60{col 51}{space 3}0.109{col 59}{space 4}-.3066507{col 72}{space 3} .0308039
{txt}{space 11}income {c |}{col 19}{res}{space 2} .1630261{col 31}{space 2} .0309176{col 42}{space 1}    5.27{col 51}{space 3}0.000{col 59}{space 4} .1024288{col 72}{space 3} .2236234
{txt}{space 5}social_class {c |}{col 19}{res}{space 2} .0105211{col 31}{space 2} .0380666{col 42}{space 1}    0.28{col 51}{space 3}0.782{col 59}{space 4} -.064088{col 72}{space 3} .0851302
{txt}{space 11}ln_gdp {c |}{col 19}{res}{space 2}-.5283591{col 31}{space 2} .3562179{col 42}{space 1}   -1.48{col 51}{space 3}0.138{col 59}{space 4}-1.226533{col 72}{space 3} .1698153
{txt}{space 4}growth_one_yr {c |}{col 19}{res}{space 2} -.040304{col 31}{space 2} .0282247{col 42}{space 1}   -1.43{col 51}{space 3}0.153{col 59}{space 4}-.0956233{col 72}{space 3} .0150153
{txt}{space 10}new_rol {c |}{col 19}{res}{space 2}-.0128512{col 31}{space 2} .0372083{col 42}{space 1}   -0.35{col 51}{space 3}0.730{col 59}{space 4}-.0857782{col 72}{space 3} .0600757
{txt}{space 10}new_gov {c |}{col 19}{res}{space 2} .0423254{col 31}{space 2}  .036743{col 42}{space 1}    1.15{col 51}{space 3}0.249{col 59}{space 4}-.0296896{col 72}{space 3} .1143404
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 8.530533{col 31}{space 2} 3.105924{col 42}{space 1}    2.75{col 51}{space 3}0.006{col 59}{space 4} 2.443035{col 72}{space 3} 14.61803
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33}  .049509{col 44} .0291691{col 58} .0156021{col 70} .1571029
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9844471{col 44} .2568301{col 58} .5903702{col 70} 1.641574
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315257{col 44} .4072814{col 58}  4.57405{col 70} 6.176572
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Use vdem_egaldem
. eststo OTH7: mixed diff_dem_egaldem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68632.435}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68632.435}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   379.03
{txt}Log pseudolikelihood = {res}-68632.435{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 82:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}diff_dem_egaldem{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}new_fotp {c |}{col 18}{res}{space 2}-.0535709{col 30}{space 2} .0274388{col 41}{space 1}   -1.95{col 50}{space 3}0.051{col 58}{space 4}  -.10735{col 71}{space 3} .0002082
{txt}{space 6}university {c |}{col 18}{res}{space 2}-.3992342{col 30}{space 2} .0584678{col 41}{space 1}   -6.83{col 50}{space 3}0.000{col 58}{space 4}-.5138291{col 71}{space 3}-.2846394
{txt}{space 10}female {c |}{col 18}{res}{space 2} .1630831{col 30}{space 2} .0545594{col 41}{space 1}    2.99{col 50}{space 3}0.003{col 58}{space 4} .0561486{col 71}{space 3} .2700175
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0215383{col 30}{space 2} .0044131{col 41}{space 1}   -4.88{col 50}{space 3}0.000{col 58}{space 4}-.0301878{col 71}{space 3}-.0128889
{txt}{space 10}age_sq {c |}{col 18}{res}{space 2} .0261518{col 30}{space 2}  .005198{col 41}{space 1}    5.03{col 50}{space 3}0.000{col 58}{space 4} .0159639{col 71}{space 3} .0363396
{txt}{space 9}married {c |}{col 18}{res}{space 2} .0300255{col 30}{space 2} .0592554{col 41}{space 1}    0.51{col 50}{space 3}0.612{col 58}{space 4}-.0861128{col 71}{space 3} .1461639
{txt}{space 6}unemployed {c |}{col 18}{res}{space 2}-.1379563{col 30}{space 2} .0860122{col 41}{space 1}   -1.60{col 50}{space 3}0.109{col 58}{space 4}-.3065371{col 71}{space 3} .0306244
{txt}{space 10}income {c |}{col 18}{res}{space 2}  .162935{col 30}{space 2} .0309709{col 41}{space 1}    5.26{col 50}{space 3}0.000{col 58}{space 4} .1022331{col 71}{space 3} .2236369
{txt}{space 4}social_class {c |}{col 18}{res}{space 2} .0104869{col 30}{space 2} .0380775{col 41}{space 1}    0.28{col 50}{space 3}0.783{col 58}{space 4}-.0641437{col 71}{space 3} .0851175
{txt}{space 10}ln_gdp {c |}{col 18}{res}{space 2}-.4561946{col 30}{space 2} .2296062{col 41}{space 1}   -1.99{col 50}{space 3}0.047{col 58}{space 4}-.9062145{col 71}{space 3}-.0061746
{txt}{space 3}growth_one_yr {c |}{col 18}{res}{space 2} -.006864{col 30}{space 2} .0279816{col 41}{space 1}   -0.25{col 50}{space 3}0.806{col 58}{space 4}-.0617068{col 71}{space 3} .0479789
{txt}{space 9}new_rol {c |}{col 18}{res}{space 2} .0284024{col 30}{space 2}  .033494{col 41}{space 1}    0.85{col 50}{space 3}0.396{col 58}{space 4}-.0372446{col 71}{space 3} .0940493
{txt}{space 9}new_gov {c |}{col 18}{res}{space 2} .0139582{col 30}{space 2}  .033682{col 41}{space 1}    0.41{col 50}{space 3}0.679{col 58}{space 4}-.0520574{col 71}{space 3} .0799738
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 6.440799{col 30}{space 2} 1.813526{col 41}{space 1}    3.55{col 50}{space 3}0.000{col 58}{space 4} 2.886354{col 71}{space 3} 9.995244
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0487855{col 44} .0284701{col 58} .0155435{col 70} .1531201
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.111613{col 44}  .290401{col 58} .6661676{col 70} 1.854915
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315291{col 44} .4072856{col 58} 4.574077{col 70} 6.176616
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo OTH8: mixed diff_dem_egaldem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68630.505}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68630.505}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   416.31
{txt}Log pseudolikelihood = {res}-68630.505{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 82:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}diff_dem_egaldem{col 18}{c |} Coefficient{col 30}  std. err.{col 42}      z{col 50}   P>|z|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}new_msf {c |}{col 18}{res}{space 2}-.0482048{col 30}{space 2} .0145081{col 41}{space 1}   -3.32{col 50}{space 3}0.001{col 58}{space 4}-.0766401{col 71}{space 3}-.0197695
{txt}{space 6}university {c |}{col 18}{res}{space 2}-.3996497{col 30}{space 2} .0584815{col 41}{space 1}   -6.83{col 50}{space 3}0.000{col 58}{space 4}-.5142713{col 71}{space 3}-.2850281
{txt}{space 10}female {c |}{col 18}{res}{space 2} .1629543{col 30}{space 2}  .054543{col 41}{space 1}    2.99{col 50}{space 3}0.003{col 58}{space 4}  .056052{col 71}{space 3} .2698566
{txt}{space 13}age {c |}{col 18}{res}{space 2}-.0215348{col 30}{space 2} .0044134{col 41}{space 1}   -4.88{col 50}{space 3}0.000{col 58}{space 4}-.0301849{col 71}{space 3}-.0128847
{txt}{space 10}age_sq {c |}{col 18}{res}{space 2} .0261391{col 30}{space 2} .0051957{col 41}{space 1}    5.03{col 50}{space 3}0.000{col 58}{space 4} .0159556{col 71}{space 3} .0363225
{txt}{space 9}married {c |}{col 18}{res}{space 2} .0299366{col 30}{space 2} .0592589{col 41}{space 1}    0.51{col 50}{space 3}0.613{col 58}{space 4}-.0862088{col 71}{space 3}  .146082
{txt}{space 6}unemployed {c |}{col 18}{res}{space 2}-.1382504{col 30}{space 2} .0859969{col 41}{space 1}   -1.61{col 50}{space 3}0.108{col 58}{space 4}-.3068011{col 71}{space 3} .0303004
{txt}{space 10}income {c |}{col 18}{res}{space 2} .1630044{col 30}{space 2} .0309638{col 41}{space 1}    5.26{col 50}{space 3}0.000{col 58}{space 4} .1023164{col 71}{space 3} .2236924
{txt}{space 4}social_class {c |}{col 18}{res}{space 2} .0104276{col 30}{space 2} .0380734{col 41}{space 1}    0.27{col 50}{space 3}0.784{col 58}{space 4} -.064195{col 71}{space 3} .0850502
{txt}{space 10}ln_gdp {c |}{col 18}{res}{space 2}-.6394283{col 30}{space 2}  .232579{col 41}{space 1}   -2.75{col 50}{space 3}0.006{col 58}{space 4}-1.095275{col 71}{space 3}-.1835818
{txt}{space 3}growth_one_yr {c |}{col 18}{res}{space 2}-.0335974{col 30}{space 2} .0243062{col 41}{space 1}   -1.38{col 50}{space 3}0.167{col 58}{space 4}-.0812366{col 71}{space 3} .0140419
{txt}{space 9}new_rol {c |}{col 18}{res}{space 2} .0042749{col 30}{space 2} .0239459{col 41}{space 1}    0.18{col 50}{space 3}0.858{col 58}{space 4}-.0426581{col 71}{space 3} .0512079
{txt}{space 9}new_gov {c |}{col 18}{res}{space 2} .0256776{col 30}{space 2} .0273053{col 41}{space 1}    0.94{col 50}{space 3}0.347{col 58}{space 4}-.0278398{col 71}{space 3}  .079195
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 8.994967{col 30}{space 2} 1.838652{col 41}{space 1}    4.89{col 50}{space 3}0.000{col 58}{space 4} 5.391276{col 71}{space 3} 12.59866
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0490383{col 44} .0285685{col 58} .0156547{col 70} .1536119
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9318862{col 44} .2965336{col 58} .4994636{col 70} 1.738689
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.31528{col 44} .4072832{col 58}  4.57407{col 70} 6.176599
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Use Polity5
. eststo OTH9: mixed diff_dem_polity new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68641.743}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68641.743}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   373.84
{txt}Log pseudolikelihood = {res}-68641.743{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_polity{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}new_fotp {c |}{col 17}{res}{space 2}  -.22764{col 29}{space 2} .0409639{col 40}{space 1}   -5.56{col 49}{space 3}0.000{col 57}{space 4}-.3079277{col 70}{space 3}-.1473523
{txt}{space 5}university {c |}{col 17}{res}{space 2}-.3990086{col 29}{space 2} .0585191{col 40}{space 1}   -6.82{col 49}{space 3}0.000{col 57}{space 4} -.513704{col 70}{space 3}-.2843133
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1632924{col 29}{space 2}  .054561{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0563549{col 70}{space 3}   .27023
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0215958{col 29}{space 2} .0044257{col 40}{space 1}   -4.88{col 49}{space 3}0.000{col 57}{space 4}-.0302699{col 70}{space 3}-.0129217
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0262304{col 29}{space 2} .0052127{col 40}{space 1}    5.03{col 49}{space 3}0.000{col 57}{space 4} .0160138{col 70}{space 3} .0364471
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0297509{col 29}{space 2} .0593762{col 40}{space 1}    0.50{col 49}{space 3}0.616{col 57}{space 4}-.0866244{col 70}{space 3} .1461261
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.1380331{col 29}{space 2} .0859653{col 40}{space 1}   -1.61{col 49}{space 3}0.108{col 57}{space 4} -.306522{col 70}{space 3} .0304557
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1628455{col 29}{space 2} .0308998{col 40}{space 1}    5.27{col 49}{space 3}0.000{col 57}{space 4}  .102283{col 70}{space 3} .2234081
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0106379{col 29}{space 2} .0380161{col 40}{space 1}    0.28{col 49}{space 3}0.780{col 57}{space 4}-.0638722{col 70}{space 3} .0851481
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2} -.120128{col 29}{space 2} .4523536{col 40}{space 1}   -0.27{col 49}{space 3}0.791{col 57}{space 4}-1.006725{col 70}{space 3} .7664688
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2} .0076589{col 29}{space 2} .0401689{col 40}{space 1}    0.19{col 49}{space 3}0.849{col 57}{space 4}-.0710706{col 70}{space 3} .0863885
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2} .1757081{col 29}{space 2} .0552369{col 40}{space 1}    3.18{col 49}{space 3}0.001{col 57}{space 4} .0674457{col 70}{space 3} .2839705
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2}-.0790254{col 29}{space 2} .0517865{col 40}{space 1}   -1.53{col 49}{space 3}0.127{col 57}{space 4}-.1805252{col 70}{space 3} .0224743
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 3.758269{col 29}{space 2} 3.488417{col 40}{space 1}    1.08{col 49}{space 3}0.281{col 57}{space 4}-3.078903{col 70}{space 3} 10.59544
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33}  .049649{col 44} .0293607{col 58} .0155789{col 70} .1582277
{txt}{space 18}var(_cons) {c |}{res}{col 33} 2.598669{col 44} .7396779{col 58} 1.487536{col 70} 4.539778
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.31525{col 44}  .407281{col 58} 4.574045{col 70} 6.176565
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo OTH10: mixed diff_dem_polity new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68643.448}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68643.448}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   527.12
{txt}Log pseudolikelihood = {res}-68643.448{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}diff_dem_polity{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}new_msf {c |}{col 17}{res}{space 2} -.139444{col 29}{space 2} .0222857{col 40}{space 1}   -6.26{col 49}{space 3}0.000{col 57}{space 4}-.1831231{col 70}{space 3}-.0957649
{txt}{space 5}university {c |}{col 17}{res}{space 2} -.399186{col 29}{space 2} .0585114{col 40}{space 1}   -6.82{col 49}{space 3}0.000{col 57}{space 4}-.5138662{col 70}{space 3}-.2845058
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1632266{col 29}{space 2} .0545566{col 40}{space 1}    2.99{col 49}{space 3}0.003{col 57}{space 4} .0562976{col 70}{space 3} .2701556
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0216056{col 29}{space 2} .0044282{col 40}{space 1}   -4.88{col 49}{space 3}0.000{col 57}{space 4}-.0302847{col 70}{space 3}-.0129265
{txt}{space 9}age_sq {c |}{col 17}{res}{space 2} .0262407{col 29}{space 2} .0052147{col 40}{space 1}    5.03{col 49}{space 3}0.000{col 57}{space 4}   .01602{col 70}{space 3} .0364614
{txt}{space 8}married {c |}{col 17}{res}{space 2} .0295628{col 29}{space 2} .0594243{col 40}{space 1}    0.50{col 49}{space 3}0.619{col 57}{space 4}-.0869066{col 70}{space 3} .1460322
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-.1383968{col 29}{space 2} .0859657{col 40}{space 1}   -1.61{col 49}{space 3}0.107{col 57}{space 4}-.3068865{col 70}{space 3} .0300928
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1628659{col 29}{space 2} .0308798{col 40}{space 1}    5.27{col 49}{space 3}0.000{col 57}{space 4} .1023427{col 70}{space 3} .2233892
{txt}{space 3}social_class {c |}{col 17}{res}{space 2} .0105965{col 29}{space 2} .0379949{col 40}{space 1}    0.28{col 49}{space 3}0.780{col 57}{space 4}-.0638722{col 70}{space 3} .0850652
{txt}{space 9}ln_gdp {c |}{col 17}{res}{space 2}-.4492591{col 29}{space 2} .5426255{col 40}{space 1}   -0.83{col 49}{space 3}0.408{col 57}{space 4}-1.512786{col 70}{space 3} .6142673
{txt}{space 2}growth_one_yr {c |}{col 17}{res}{space 2}-.0567914{col 29}{space 2} .0487302{col 40}{space 1}   -1.17{col 49}{space 3}0.244{col 57}{space 4}-.1523009{col 70}{space 3} .0387181
{txt}{space 8}new_rol {c |}{col 17}{res}{space 2} .0617941{col 29}{space 2} .0567284{col 40}{space 1}    1.09{col 49}{space 3}0.276{col 57}{space 4}-.0493915{col 70}{space 3} .1729797
{txt}{space 8}new_gov {c |}{col 17}{res}{space 2}-.0201266{col 29}{space 2} .0553637{col 40}{space 1}   -0.36{col 49}{space 3}0.716{col 57}{space 4}-.1286375{col 70}{space 3} .0883843
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 7.832893{col 29}{space 2} 4.712878{col 40}{space 1}    1.66{col 49}{space 3}0.097{col 57}{space 4}-1.404178{col 70}{space 3} 17.06997
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33}  .049722{col 44}  .029424{col 58} .0155896{col 70} .1585855
{txt}{space 18}var(_cons) {c |}{res}{col 33} 3.035217{col 44} .6966792{col 58} 1.935583{col 70} 4.759569
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315247{col 44} .4072819{col 58}  4.57404{col 70} 6.176564
{txt}{hline 29}{c BT}{hline 48}

{com}. 
. // Use Freedom in the World
. eststo OTH11: mixed diff_dem_fitw new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68632.834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68632.834}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   249.36
{txt}Log pseudolikelihood = {res}-68632.834{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_fitw{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1198217{col 27}{space 2} .0310271{col 38}{space 1}   -3.86{col 47}{space 3}0.000{col 55}{space 4}-.1806336{col 68}{space 3}-.0590097
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3989762{col 27}{space 2} .0584463{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5135288{col 68}{space 3}-.2844235
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1631671{col 27}{space 2} .0545602{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4}  .056231{col 68}{space 3} .2701032
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0215889{col 27}{space 2} .0044255{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-.0302626{col 68}{space 3}-.0129151
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0262179{col 27}{space 2} .0052127{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0160013{col 68}{space 3} .0364345
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0298416{col 27}{space 2}  .059392{col 38}{space 1}    0.50{col 47}{space 3}0.615{col 55}{space 4}-.0865647{col 68}{space 3} .1462479
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1375823{col 27}{space 2} .0860455{col 38}{space 1}   -1.60{col 47}{space 3}0.110{col 55}{space 4}-.3062283{col 68}{space 3} .0310638
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1629177{col 27}{space 2} .0309218{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1023122{col 68}{space 3} .2235233
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0106392{col 27}{space 2} .0380195{col 38}{space 1}    0.28{col 47}{space 3}0.780{col 55}{space 4}-.0638776{col 68}{space 3} .0851559
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.4813206{col 27}{space 2} .2512217{col 38}{space 1}   -1.92{col 47}{space 3}0.055{col 55}{space 4}-.9737061{col 68}{space 3}  .011065
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0228984{col 27}{space 2} .0269335{col 38}{space 1}   -0.85{col 47}{space 3}0.395{col 55}{space 4}-.0756871{col 68}{space 3} .0298903
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0049713{col 27}{space 2} .0456164{col 38}{space 1}    0.11{col 47}{space 3}0.913{col 55}{space 4}-.0844352{col 68}{space 3} .0943778
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0189061{col 27}{space 2} .0310945{col 38}{space 1}    0.61{col 47}{space 3}0.543{col 55}{space 4} -.042038{col 68}{space 3} .0798502
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 8.428491{col 27}{space 2} 2.142323{col 38}{space 1}    3.93{col 47}{space 3}0.000{col 55}{space 4} 4.229615{col 68}{space 3} 12.62737
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0490233{col 44} .0289192{col 58} .0154267{col 70} .1557876
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.153211{col 44} .3670248{col 58} .6180199{col 70} 2.151865
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315279{col 44} .4072869{col 58} 4.574063{col 70} 6.176607
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo OTH12: mixed diff_dem_fitw new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-68634.185}  
Iteration 1:{space 3}log pseudolikelihood = {res:-68634.185}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    30,414
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,382.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   266.74
{txt}Log pseudolikelihood = {res}-68634.185{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_fitw{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0721297{col 27}{space 2} .0174699{col 38}{space 1}   -4.13{col 47}{space 3}0.000{col 55}{space 4}  -.10637{col 68}{space 3}-.0378894
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3991996{col 27}{space 2}  .058443{col 38}{space 1}   -6.83{col 47}{space 3}0.000{col 55}{space 4}-.5137459{col 68}{space 3}-.2846533
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1630998{col 27}{space 2} .0545508{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0561821{col 68}{space 3} .2700174
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0216013{col 27}{space 2}  .004428{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-.0302801{col 68}{space 3}-.0129224
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0262304{col 27}{space 2} .0052144{col 38}{space 1}    5.03{col 47}{space 3}0.000{col 55}{space 4} .0160104{col 68}{space 3} .0364503
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0296291{col 27}{space 2} .0594623{col 38}{space 1}    0.50{col 47}{space 3}0.618{col 55}{space 4}-.0869149{col 68}{space 3} .1461731
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1380771{col 27}{space 2} .0860411{col 38}{space 1}   -1.60{col 47}{space 3}0.109{col 55}{space 4}-.3067145{col 68}{space 3} .0305604
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1629412{col 27}{space 2} .0308964{col 38}{space 1}    5.27{col 47}{space 3}0.000{col 55}{space 4} .1023854{col 68}{space 3}  .223497
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0105892{col 27}{space 2} .0379906{col 38}{space 1}    0.28{col 47}{space 3}0.780{col 55}{space 4} -.063871{col 68}{space 3} .0850494
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6458087{col 27}{space 2} .3234901{col 38}{space 1}   -2.00{col 47}{space 3}0.046{col 55}{space 4}-1.279838{col 68}{space 3}-.0117798
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0558855{col 27}{space 2} .0315198{col 38}{space 1}   -1.77{col 47}{space 3}0.076{col 55}{space 4}-.1176632{col 68}{space 3} .0058922
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0552285{col 27}{space 2} .0427005{col 38}{space 1}   -1.29{col 47}{space 3}0.196{col 55}{space 4}-.1389199{col 68}{space 3} .0284628
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0500986{col 27}{space 2} .0317731{col 38}{space 1}    1.58{col 47}{space 3}0.115{col 55}{space 4}-.0121754{col 68}{space 3} .1123727
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 10.44193{col 27}{space 2} 2.915843{col 38}{space 1}    3.58{col 47}{space 3}0.000{col 55}{space 4} 4.726987{col 68}{space 3} 16.15688
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0491943{col 44} .0290701{col 58} .0154497{col 70} .1566426
{txt}{space 18}var(_cons) {c |}{res}{col 33}  1.30464{col 44} .3874908{col 58} .7289128{col 70} 2.335102
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.315271{col 44} .4072877{col 58} 4.574054{col 70} 6.176601
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 15
. /* Drop respondents from particular countries */
. // Drop Chinese respondents
. eststo CHN1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if code != 156 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64999.624}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64999.624}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,690
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,366.2
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   517.60
{txt}Log pseudolikelihood = {res}-64999.624{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0966856{col 27}{space 2} .0240237{col 38}{space 1}   -4.02{col 47}{space 3}0.000{col 55}{space 4}-.1437713{col 68}{space 3}-.0495999
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3946268{col 27}{space 2} .0616478{col 38}{space 1}   -6.40{col 47}{space 3}0.000{col 55}{space 4}-.5154544{col 68}{space 3}-.2737993
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1705197{col 27}{space 2} .0575905{col 38}{space 1}    2.96{col 47}{space 3}0.003{col 55}{space 4} .0576444{col 68}{space 3}  .283395
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0219823{col 27}{space 2} .0045946{col 38}{space 1}   -4.78{col 47}{space 3}0.000{col 55}{space 4}-.0309876{col 68}{space 3}-.0129771
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0264773{col 27}{space 2} .0054377{col 38}{space 1}    4.87{col 47}{space 3}0.000{col 55}{space 4} .0158196{col 68}{space 3} .0371351
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0329497{col 27}{space 2} .0615355{col 38}{space 1}    0.54{col 47}{space 3}0.592{col 55}{space 4}-.0876577{col 68}{space 3} .1535572
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1306524{col 27}{space 2} .0879206{col 38}{space 1}   -1.49{col 47}{space 3}0.137{col 55}{space 4}-.3029737{col 68}{space 3} .0416689
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1643407{col 27}{space 2} .0325022{col 38}{space 1}    5.06{col 47}{space 3}0.000{col 55}{space 4} .1006376{col 68}{space 3} .2280438
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0051144{col 27}{space 2} .0390476{col 38}{space 1}    0.13{col 47}{space 3}0.896{col 55}{space 4}-.0714176{col 68}{space 3} .0816464
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5796051{col 27}{space 2} .2459522{col 38}{space 1}   -2.36{col 47}{space 3}0.018{col 55}{space 4}-1.061663{col 68}{space 3}-.0975476
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0145634{col 27}{space 2} .0234629{col 38}{space 1}   -0.62{col 47}{space 3}0.535{col 55}{space 4}-.0605498{col 68}{space 3} .0314231
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0275666{col 27}{space 2} .0399163{col 38}{space 1}    0.69{col 47}{space 3}0.490{col 55}{space 4}-.0506679{col 68}{space 3} .1058011
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2}  .035069{col 27}{space 2} .0318348{col 38}{space 1}    1.10{col 47}{space 3}0.271{col 55}{space 4}-.0273261{col 68}{space 3} .0974642
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.918793{col 27}{space 2} 1.988486{col 38}{space 1}    3.48{col 47}{space 3}0.001{col 55}{space 4} 3.021432{col 68}{space 3} 10.81615
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0536251{col 44}  .031153{col 58}  .017174{col 70} .1674424
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9638548{col 44} .1976317{col 58} .6448797{col 70} 1.440604
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.411848{col 44}  .419782{col 58} 4.648579{col 70} 6.300441
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo CHN2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if code != 156 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64999.722}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64999.722}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,690
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,366.2
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   504.34
{txt}Log pseudolikelihood = {res}-64999.722{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0637475{col 27}{space 2} .0109193{col 38}{space 1}   -5.84{col 47}{space 3}0.000{col 55}{space 4} -.085149{col 68}{space 3} -.042346
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3949632{col 27}{space 2} .0616381{col 38}{space 1}   -6.41{col 47}{space 3}0.000{col 55}{space 4}-.5157718{col 68}{space 3}-.2741547
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1704035{col 27}{space 2} .0575783{col 38}{space 1}    2.96{col 47}{space 3}0.003{col 55}{space 4}  .057552{col 68}{space 3}  .283255
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0219953{col 27}{space 2} .0045982{col 38}{space 1}   -4.78{col 47}{space 3}0.000{col 55}{space 4}-.0310077{col 68}{space 3} -.012983
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0264876{col 27}{space 2} .0054394{col 38}{space 1}    4.87{col 47}{space 3}0.000{col 55}{space 4} .0158265{col 68}{space 3} .0371487
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0327531{col 27}{space 2} .0615959{col 38}{space 1}    0.53{col 47}{space 3}0.595{col 55}{space 4}-.0879728{col 68}{space 3} .1534789
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1311988{col 27}{space 2} .0878913{col 38}{space 1}   -1.49{col 47}{space 3}0.136{col 55}{space 4}-.3034625{col 68}{space 3} .0410649
{txt}{space 7}income {c |}{col 15}{res}{space 2}  .164384{col 27}{space 2} .0324677{col 38}{space 1}    5.06{col 47}{space 3}0.000{col 55}{space 4} .1007484{col 68}{space 3} .2280196
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0050401{col 27}{space 2} .0390114{col 38}{space 1}    0.13{col 47}{space 3}0.897{col 55}{space 4}-.0714208{col 68}{space 3}  .081501
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7496586{col 27}{space 2} .2953608{col 38}{space 1}   -2.54{col 47}{space 3}0.011{col 55}{space 4}-1.328555{col 68}{space 3} -.170762
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0447589{col 27}{space 2} .0286549{col 38}{space 1}   -1.56{col 47}{space 3}0.118{col 55}{space 4}-.1009215{col 68}{space 3} .0114037
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0215704{col 27}{space 2}  .039406{col 38}{space 1}   -0.55{col 47}{space 3}0.584{col 55}{space 4}-.0988048{col 68}{space 3}  .055664
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0608896{col 27}{space 2} .0325698{col 38}{space 1}    1.87{col 47}{space 3}0.062{col 55}{space 4}-.0029459{col 68}{space 3} .1247252
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.121253{col 27}{space 2} 2.522418{col 38}{space 1}    3.62{col 47}{space 3}0.000{col 55}{space 4} 4.177404{col 68}{space 3}  14.0651
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0538765{col 44} .0313782{col 58} .0172048{col 70} .1687127
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9730891{col 44} .3155903{col 58} .5153385{col 70} 1.837438
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.411837{col 44} .4197812{col 58}  4.64857{col 70} 6.300428
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 16
. // Drop Russian respondents
. eststo RUS1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if code != 643 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64198.583}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64198.583}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,417
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,353.2
{txt}{col 63}max{col 67}={col 69}{res}     1,921
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   385.88
{txt}Log pseudolikelihood = {res}-64198.583{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1104484{col 27}{space 2} .0273542{col 38}{space 1}   -4.04{col 47}{space 3}0.000{col 55}{space 4}-.1640616{col 68}{space 3}-.0568353
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4092531{col 27}{space 2} .0613194{col 38}{space 1}   -6.67{col 47}{space 3}0.000{col 55}{space 4} -.529437{col 68}{space 3}-.2890693
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1628504{col 27}{space 2} .0584283{col 38}{space 1}    2.79{col 47}{space 3}0.005{col 55}{space 4}  .048333{col 68}{space 3} .2773678
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0217213{col 27}{space 2} .0047529{col 38}{space 1}   -4.57{col 47}{space 3}0.000{col 55}{space 4}-.0310367{col 68}{space 3}-.0124058
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0272597{col 27}{space 2} .0056428{col 38}{space 1}    4.83{col 47}{space 3}0.000{col 55}{space 4}    .0162{col 68}{space 3} .0383194
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0142682{col 27}{space 2} .0622174{col 38}{space 1}    0.23{col 47}{space 3}0.819{col 55}{space 4}-.1076757{col 68}{space 3}  .136212
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1494188{col 27}{space 2} .0875741{col 38}{space 1}   -1.71{col 47}{space 3}0.088{col 55}{space 4}-.3210608{col 68}{space 3} .0222232
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1568048{col 27}{space 2} .0320163{col 38}{space 1}    4.90{col 47}{space 3}0.000{col 55}{space 4} .0940539{col 68}{space 3} .2195556
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}  .012868{col 27}{space 2} .0403086{col 38}{space 1}    0.32{col 47}{space 3}0.750{col 55}{space 4}-.0661355{col 68}{space 3} .0918715
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.4721603{col 27}{space 2} .2754509{col 38}{space 1}   -1.71{col 47}{space 3}0.087{col 55}{space 4}-1.012034{col 68}{space 3} .0677135
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0016828{col 27}{space 2} .0243716{col 38}{space 1}   -0.07{col 47}{space 3}0.945{col 55}{space 4}-.0494503{col 68}{space 3} .0460846
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0249345{col 27}{space 2}  .041611{col 38}{space 1}    0.60{col 47}{space 3}0.549{col 55}{space 4}-.0566216{col 68}{space 3} .1064907
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0355617{col 27}{space 2} .0338085{col 38}{space 1}    1.05{col 47}{space 3}0.293{col 55}{space 4}-.0307017{col 68}{space 3}  .101825
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.544115{col 27}{space 2} 2.170744{col 38}{space 1}    3.01{col 47}{space 3}0.003{col 55}{space 4} 2.289534{col 68}{space 3}  10.7987
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0487966{col 44} .0309052{col 58}  .014102{col 70} .1688482
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9751386{col 44} .1990372{col 58} .6536212{col 70} 1.454811
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.342701{col 44} .4355072{col 58} 4.553818{col 70} 6.268247
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo RUS2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if code != 643 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-64198.788}  
Iteration 1:{space 3}log pseudolikelihood = {res:-64198.788}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    28,417
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        21
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,353.2
{txt}{col 63}max{col 67}={col 69}{res}     1,921
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   363.66
{txt}Log pseudolikelihood = {res}-64198.788{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:21} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0709184{col 27}{space 2} .0125836{col 38}{space 1}   -5.64{col 47}{space 3}0.000{col 55}{space 4}-.0955819{col 68}{space 3}-.0462549
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4095928{col 27}{space 2} .0613008{col 38}{space 1}   -6.68{col 47}{space 3}0.000{col 55}{space 4}-.5297401{col 68}{space 3}-.2894455
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1627379{col 27}{space 2} .0584133{col 38}{space 1}    2.79{col 47}{space 3}0.005{col 55}{space 4} .0482498{col 68}{space 3} .2772259
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0217379{col 27}{space 2} .0047567{col 38}{space 1}   -4.57{col 47}{space 3}0.000{col 55}{space 4}-.0310608{col 68}{space 3}-.0124149
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0272722{col 27}{space 2}  .005645{col 38}{space 1}    4.83{col 47}{space 3}0.000{col 55}{space 4} .0162082{col 68}{space 3} .0383361
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0140742{col 27}{space 2} .0623065{col 38}{space 1}    0.23{col 47}{space 3}0.821{col 55}{space 4}-.1080443{col 68}{space 3} .1361927
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} -.149954{col 27}{space 2} .0875436{col 38}{space 1}   -1.71{col 47}{space 3}0.087{col 55}{space 4}-.3215362{col 68}{space 3} .0216283
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1568677{col 27}{space 2} .0319827{col 38}{space 1}    4.90{col 47}{space 3}0.000{col 55}{space 4} .0941828{col 68}{space 3} .2195526
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0128225{col 27}{space 2} .0402749{col 38}{space 1}    0.32{col 47}{space 3}0.750{col 55}{space 4}-.0661148{col 68}{space 3} .0917598
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6891732{col 27}{space 2} .3203257{col 38}{space 1}   -2.15{col 47}{space 3}0.031{col 55}{space 4}   -1.317{col 68}{space 3}-.0613463
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0382126{col 27}{space 2} .0302099{col 38}{space 1}   -1.26{col 47}{space 3}0.206{col 55}{space 4} -.097423{col 68}{space 3} .0209978
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0297698{col 27}{space 2} .0388395{col 38}{space 1}   -0.77{col 47}{space 3}0.443{col 55}{space 4}-.1058939{col 68}{space 3} .0463542
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0649261{col 27}{space 2} .0317576{col 38}{space 1}    2.04{col 47}{space 3}0.041{col 55}{space 4} .0026822{col 68}{space 3} .1271699
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.117249{col 27}{space 2}   2.7466{col 38}{space 1}    3.32{col 47}{space 3}0.001{col 55}{space 4} 3.734011{col 68}{space 3} 14.50049
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0489929{col 44} .0311353{col 58}  .014099{col 70}  .170246
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9944473{col 44} .2858501{col 58} .5661182{col 70} 1.746853
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.342692{col 44} .4355078{col 58} 4.553809{col 70}  6.26824
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 17
. // Drop Chinese and Russian respondents
. eststo CNR1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if code != 156 & code != 643 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -60565.62}  
Iteration 1:{space 3}log pseudolikelihood = {res: -60565.62}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    26,693
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        20
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,334.7
{txt}{col 63}max{col 67}={col 69}{res}     1,921
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   520.74
{txt}Log pseudolikelihood = {res} -60565.62{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:20} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2} -.101597{col 27}{space 2} .0264055{col 38}{space 1}   -3.85{col 47}{space 3}0.000{col 55}{space 4}-.1533507{col 68}{space 3}-.0498432
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4053599{col 27}{space 2} .0648336{col 38}{space 1}   -6.25{col 47}{space 3}0.000{col 55}{space 4}-.5324314{col 68}{space 3}-.2782883
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1706003{col 27}{space 2}  .061973{col 38}{space 1}    2.75{col 47}{space 3}0.006{col 55}{space 4} .0491355{col 68}{space 3} .2920652
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0221964{col 27}{space 2} .0049533{col 38}{space 1}   -4.48{col 47}{space 3}0.000{col 55}{space 4}-.0319047{col 68}{space 3} -.012488
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0276455{col 27}{space 2} .0059148{col 38}{space 1}    4.67{col 47}{space 3}0.000{col 55}{space 4} .0160526{col 68}{space 3} .0392383
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0168021{col 27}{space 2} .0647571{col 38}{space 1}    0.26{col 47}{space 3}0.795{col 55}{space 4}-.1101195{col 68}{space 3} .1437238
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1423085{col 27}{space 2} .0895808{col 38}{space 1}   -1.59{col 47}{space 3}0.112{col 55}{space 4}-.3178837{col 68}{space 3} .0332667
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1579225{col 27}{space 2} .0337373{col 38}{space 1}    4.68{col 47}{space 3}0.000{col 55}{space 4} .0917987{col 68}{space 3} .2240464
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0071595{col 27}{space 2} .0414656{col 38}{space 1}    0.17{col 47}{space 3}0.863{col 55}{space 4}-.0741116{col 68}{space 3} .0884306
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.4679881{col 27}{space 2} .2722251{col 38}{space 1}   -1.72{col 47}{space 3}0.086{col 55}{space 4}-1.001539{col 68}{space 3} .0655634
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0052175{col 27}{space 2} .0250209{col 38}{space 1}   -0.21{col 47}{space 3}0.835{col 55}{space 4}-.0542575{col 68}{space 3} .0438226
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0287134{col 27}{space 2} .0403941{col 38}{space 1}    0.71{col 47}{space 3}0.477{col 55}{space 4}-.0504575{col 68}{space 3} .1078843
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2}  .030633{col 27}{space 2}  .032355{col 38}{space 1}    0.95{col 47}{space 3}0.344{col 55}{space 4}-.0327817{col 68}{space 3} .0940477
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.247943{col 27}{space 2} 2.134349{col 38}{space 1}    2.93{col 47}{space 3}0.003{col 55}{space 4} 2.064696{col 68}{space 3} 10.43119
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0539176{col 44} .0331397{col 58} .0161641{col 70} .1798496
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9432074{col 44} .1939397{col 58} .6303555{col 70} 1.411331
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.448341{col 44} .4500241{col 58} 4.634004{col 70} 6.405782
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo CNR2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if code != 156 & code != 643 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-60566.042}  
Iteration 1:{space 3}log pseudolikelihood = {res:-60566.042}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    26,693
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        20
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,334.7
{txt}{col 63}max{col 67}={col 69}{res}     1,921
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   468.34
{txt}Log pseudolikelihood = {res}-60566.042{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:20} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0650054{col 27}{space 2} .0115769{col 38}{space 1}   -5.62{col 47}{space 3}0.000{col 55}{space 4}-.0876956{col 68}{space 3}-.0423152
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4056831{col 27}{space 2} .0648079{col 38}{space 1}   -6.26{col 47}{space 3}0.000{col 55}{space 4}-.5327043{col 68}{space 3}-.2786619
{txt}{space 7}female {c |}{col 15}{res}{space 2}  .170485{col 27}{space 2} .0619612{col 38}{space 1}    2.75{col 47}{space 3}0.006{col 55}{space 4} .0490434{col 68}{space 3} .2919266
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0222137{col 27}{space 2} .0049572{col 38}{space 1}   -4.48{col 47}{space 3}0.000{col 55}{space 4}-.0319297{col 68}{space 3}-.0124977
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0276595{col 27}{space 2}  .005917{col 38}{space 1}    4.67{col 47}{space 3}0.000{col 55}{space 4} .0160624{col 68}{space 3} .0392566
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0166301{col 27}{space 2} .0648367{col 38}{space 1}    0.26{col 47}{space 3}0.798{col 55}{space 4}-.1104475{col 68}{space 3} .1437076
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1428662{col 27}{space 2} .0895483{col 38}{space 1}   -1.60{col 47}{space 3}0.111{col 55}{space 4}-.3183777{col 68}{space 3} .0326453
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1579716{col 27}{space 2} .0336989{col 38}{space 1}    4.69{col 47}{space 3}0.000{col 55}{space 4} .0919229{col 68}{space 3} .2240202
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0070982{col 27}{space 2} .0414312{col 38}{space 1}    0.17{col 47}{space 3}0.864{col 55}{space 4}-.0741055{col 68}{space 3} .0883019
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6639849{col 27}{space 2} .3275356{col 38}{space 1}   -2.03{col 47}{space 3}0.043{col 55}{space 4}-1.305943{col 68}{space 3} -.022027
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0380816{col 27}{space 2} .0306443{col 38}{space 1}   -1.24{col 47}{space 3}0.214{col 55}{space 4}-.0981433{col 68}{space 3} .0219801
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0228086{col 27}{space 2} .0394319{col 38}{space 1}   -0.58{col 47}{space 3}0.563{col 55}{space 4}-.1000937{col 68}{space 3} .0544765
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0587077{col 27}{space 2} .0325864{col 38}{space 1}    1.80{col 47}{space 3}0.072{col 55}{space 4}-.0051604{col 68}{space 3} .1225759
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 8.582616{col 27}{space 2} 2.756426{col 38}{space 1}    3.11{col 47}{space 3}0.002{col 55}{space 4} 3.180121{col 68}{space 3} 13.98511
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0541099{col 44} .0333837{col 58}  .016148{col 70} .1813156
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9841812{col 44} .3069678{col 58} .5340524{col 70} 1.813704
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.448333{col 44} .4500247{col 58} 4.633995{col 70} 6.405775
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 18
. // Drop Pakistani, Thai, and Ukrainian respondents
. eststo PTU1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if code != 586 & code != 764 & code != 804 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-59915.574}  
Iteration 1:{space 3}log pseudolikelihood = {res:-59915.574}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    26,610
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        19
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,400.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   348.48
{txt}Log pseudolikelihood = {res}-59915.574{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:19} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1064187{col 27}{space 2} .0307255{col 38}{space 1}   -3.46{col 47}{space 3}0.001{col 55}{space 4}-.1666396{col 68}{space 3}-.0461978
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3886584{col 27}{space 2} .0551694{col 38}{space 1}   -7.04{col 47}{space 3}0.000{col 55}{space 4}-.4967884{col 68}{space 3}-.2805284
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1966029{col 27}{space 2} .0594064{col 38}{space 1}    3.31{col 47}{space 3}0.001{col 55}{space 4} .0801685{col 68}{space 3} .3130373
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.020138{col 27}{space 2} .0047371{col 38}{space 1}   -4.25{col 47}{space 3}0.000{col 55}{space 4}-.0294225{col 68}{space 3}-.0108535
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0256652{col 27}{space 2} .0057645{col 38}{space 1}    4.45{col 47}{space 3}0.000{col 55}{space 4} .0143669{col 68}{space 3} .0369635
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0114594{col 27}{space 2} .0632242{col 38}{space 1}    0.18{col 47}{space 3}0.856{col 55}{space 4}-.1124577{col 68}{space 3} .1353765
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1362342{col 27}{space 2} .0915731{col 38}{space 1}   -1.49{col 47}{space 3}0.137{col 55}{space 4}-.3157142{col 68}{space 3} .0432458
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1713504{col 27}{space 2} .0283144{col 38}{space 1}    6.05{col 47}{space 3}0.000{col 55}{space 4} .1158551{col 68}{space 3} .2268456
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0000229{col 27}{space 2}  .036958{col 38}{space 1}    0.00{col 47}{space 3}1.000{col 55}{space 4}-.0724136{col 68}{space 3} .0724593
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5567763{col 27}{space 2} .2659096{col 38}{space 1}   -2.09{col 47}{space 3}0.036{col 55}{space 4} -1.07795{col 68}{space 3}-.0356031
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0053959{col 27}{space 2} .0235564{col 38}{space 1}   -0.23{col 47}{space 3}0.819{col 55}{space 4}-.0515657{col 68}{space 3} .0407738
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0283294{col 27}{space 2} .0431765{col 38}{space 1}    0.66{col 47}{space 3}0.512{col 55}{space 4}-.0562951{col 68}{space 3} .1129539
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0341565{col 27}{space 2} .0352139{col 38}{space 1}    0.97{col 47}{space 3}0.332{col 55}{space 4}-.0348616{col 68}{space 3} .1031745
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.952431{col 27}{space 2} 2.206223{col 38}{space 1}    3.15{col 47}{space 3}0.002{col 55}{space 4} 2.628314{col 68}{space 3} 11.27655
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0248751{col 44} .0220443{col 58} .0043796{col 70} .1412862
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.138643{col 44} .2068291{col 58}  .797574{col 70} 1.625565
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.263903{col 44} .4656545{col 58} 4.425976{col 70} 6.260467
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo PTU2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if code != 586 & code != 764 & code != 804 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-59915.164}  
Iteration 1:{space 3}log pseudolikelihood = {res:-59915.164}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    26,610
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        19
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       854
{txt}{col 63}avg{col 67}={col 69}{res}   1,400.5
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   271.44
{txt}Log pseudolikelihood = {res}-59915.164{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:19} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0723388{col 27}{space 2} .0143347{col 38}{space 1}   -5.05{col 47}{space 3}0.000{col 55}{space 4}-.1004343{col 68}{space 3}-.0442433
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3888748{col 27}{space 2} .0551636{col 38}{space 1}   -7.05{col 47}{space 3}0.000{col 55}{space 4}-.4969935{col 68}{space 3}-.2807562
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1964854{col 27}{space 2} .0594008{col 38}{space 1}    3.31{col 47}{space 3}0.001{col 55}{space 4} .0800619{col 68}{space 3} .3129089
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0201289{col 27}{space 2} .0047384{col 38}{space 1}   -4.25{col 47}{space 3}0.000{col 55}{space 4} -.029416{col 68}{space 3}-.0108418
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2}  .025654{col 27}{space 2} .0057656{col 38}{space 1}    4.45{col 47}{space 3}0.000{col 55}{space 4} .0143537{col 68}{space 3} .0369543
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0111328{col 27}{space 2} .0632957{col 38}{space 1}    0.18{col 47}{space 3}0.860{col 55}{space 4}-.1129244{col 68}{space 3} .1351901
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1366365{col 27}{space 2} .0915317{col 38}{space 1}   -1.49{col 47}{space 3}0.135{col 55}{space 4}-.3160353{col 68}{space 3} .0427624
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1713688{col 27}{space 2} .0282879{col 38}{space 1}    6.06{col 47}{space 3}0.000{col 55}{space 4} .1159256{col 68}{space 3}  .226812
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-5.49e-06{col 27}{space 2} .0369351{col 38}{space 1}   -0.00{col 47}{space 3}1.000{col 55}{space 4}-.0723969{col 68}{space 3} .0723859
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7316236{col 27}{space 2} .3131998{col 38}{space 1}   -2.34{col 47}{space 3}0.019{col 55}{space 4}-1.345484{col 68}{space 3}-.1177634
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0400257{col 27}{space 2} .0326445{col 38}{space 1}   -1.23{col 47}{space 3}0.220{col 55}{space 4}-.1040077{col 68}{space 3} .0239563
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0234096{col 27}{space 2} .0382269{col 38}{space 1}   -0.61{col 47}{space 3}0.540{col 55}{space 4} -.098333{col 68}{space 3} .0515138
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0611603{col 27}{space 2} .0316482{col 38}{space 1}    1.93{col 47}{space 3}0.053{col 55}{space 4} -.000869{col 68}{space 3} .1231895
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.307447{col 27}{space 2} 2.790369{col 38}{space 1}    3.34{col 47}{space 3}0.001{col 55}{space 4} 3.838424{col 68}{space 3} 14.77647
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0249314{col 44} .0221396{col 58} .0043739{col 70} .1421115
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.090401{col 44} .3253957{col 58} .6075365{col 70} 1.957044
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.263899{col 44} .4656568{col 58} 4.425968{col 70} 6.260469
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 19
. /* Address concerns about preference falsification */
. // Drop high-income respondents (who are more likely to lie in nondemocracies)
. eststo PF1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if income <= 6 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-62224.907}  
Iteration 1:{space 3}log pseudolikelihood = {res:-62224.907}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    27,676
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       813
{txt}{col 63}avg{col 67}={col 69}{res}   1,258.0
{txt}{col 63}max{col 67}={col 69}{res}     1,931
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   251.23
{txt}Log pseudolikelihood = {res}-62224.907{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1046935{col 27}{space 2} .0255382{col 38}{space 1}   -4.10{col 47}{space 3}0.000{col 55}{space 4}-.1547474{col 68}{space 3}-.0546396
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4138676{col 27}{space 2} .0624775{col 38}{space 1}   -6.62{col 47}{space 3}0.000{col 55}{space 4}-.5363212{col 68}{space 3}-.2914139
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1729848{col 27}{space 2} .0560017{col 38}{space 1}    3.09{col 47}{space 3}0.002{col 55}{space 4} .0632235{col 68}{space 3}  .282746
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0192602{col 27}{space 2} .0046106{col 38}{space 1}   -4.18{col 47}{space 3}0.000{col 55}{space 4}-.0282967{col 68}{space 3}-.0102236
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0241178{col 27}{space 2} .0053191{col 38}{space 1}    4.53{col 47}{space 3}0.000{col 55}{space 4} .0136925{col 68}{space 3} .0345431
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0121269{col 27}{space 2} .0605415{col 38}{space 1}    0.20{col 47}{space 3}0.841{col 55}{space 4}-.1065322{col 68}{space 3}  .130786
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1085226{col 27}{space 2} .0880204{col 38}{space 1}   -1.23{col 47}{space 3}0.218{col 55}{space 4}-.2810395{col 68}{space 3} .0639943
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1531792{col 27}{space 2} .0370434{col 38}{space 1}    4.14{col 47}{space 3}0.000{col 55}{space 4} .0805754{col 68}{space 3}  .225783
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0254689{col 27}{space 2} .0373278{col 38}{space 1}    0.68{col 47}{space 3}0.495{col 55}{space 4}-.0476922{col 68}{space 3}   .09863
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5791665{col 27}{space 2}  .256349{col 38}{space 1}   -2.26{col 47}{space 3}0.024{col 55}{space 4}-1.081601{col 68}{space 3}-.0767317
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0120135{col 27}{space 2} .0228348{col 38}{space 1}   -0.53{col 47}{space 3}0.599{col 55}{space 4}-.0567688{col 68}{space 3} .0327418
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0219348{col 27}{space 2} .0410946{col 38}{space 1}    0.53{col 47}{space 3}0.594{col 55}{space 4}-.0586093{col 68}{space 3} .1024788
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0417902{col 27}{space 2} .0327049{col 38}{space 1}    1.28{col 47}{space 3}0.201{col 55}{space 4}-.0223102{col 68}{space 3} .1058906
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.106541{col 27}{space 2} 2.105095{col 38}{space 1}    3.38{col 47}{space 3}0.001{col 55}{space 4}  2.98063{col 68}{space 3} 11.23245
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0427966{col 44} .0252685{col 58} .0134534{col 70} .1361404
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.038188{col 44} .1986167{col 58} .7135625{col 70} 1.510498
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.226836{col 44} .3807604{col 58} 4.531388{col 70} 6.029018
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo PF2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if income <= 6 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-62224.628}  
Iteration 1:{space 3}log pseudolikelihood = {res:-62224.628}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    27,676
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       813
{txt}{col 63}avg{col 67}={col 69}{res}   1,258.0
{txt}{col 63}max{col 67}={col 69}{res}     1,931
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   267.83
{txt}Log pseudolikelihood = {res}-62224.628{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0695581{col 27}{space 2} .0122112{col 38}{space 1}   -5.70{col 47}{space 3}0.000{col 55}{space 4}-.0934917{col 68}{space 3}-.0456245
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4141779{col 27}{space 2} .0624721{col 38}{space 1}   -6.63{col 47}{space 3}0.000{col 55}{space 4} -.536621{col 68}{space 3}-.2917348
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1728571{col 27}{space 2} .0559885{col 38}{space 1}    3.09{col 47}{space 3}0.002{col 55}{space 4} .0631216{col 68}{space 3} .2825926
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0192704{col 27}{space 2} .0046136{col 38}{space 1}   -4.18{col 47}{space 3}0.000{col 55}{space 4}-.0283129{col 68}{space 3}-.0102279
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0241251{col 27}{space 2}   .00532{col 38}{space 1}    4.53{col 47}{space 3}0.000{col 55}{space 4} .0136981{col 68}{space 3} .0345521
{txt}{space 6}married {c |}{col 15}{res}{space 2}  .011906{col 27}{space 2} .0606143{col 38}{space 1}    0.20{col 47}{space 3}0.844{col 55}{space 4}-.1068958{col 68}{space 3} .1307079
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1090633{col 27}{space 2} .0880069{col 38}{space 1}   -1.24{col 47}{space 3}0.215{col 55}{space 4}-.2815537{col 68}{space 3} .0634271
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1532373{col 27}{space 2} .0370105{col 38}{space 1}    4.14{col 47}{space 3}0.000{col 55}{space 4}  .080698{col 68}{space 3} .2257766
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0253878{col 27}{space 2} .0372958{col 38}{space 1}    0.68{col 47}{space 3}0.496{col 55}{space 4}-.0477107{col 68}{space 3} .0984862
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7677999{col 27}{space 2} .2932434{col 38}{space 1}   -2.62{col 47}{space 3}0.009{col 55}{space 4}-1.342546{col 68}{space 3}-.1930533
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0457465{col 27}{space 2} .0281428{col 38}{space 1}   -1.63{col 47}{space 3}0.104{col 55}{space 4}-.1009053{col 68}{space 3} .0094123
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0295315{col 27}{space 2}   .03895{col 38}{space 1}   -0.76{col 47}{space 3}0.448{col 55}{space 4}-.1058721{col 68}{space 3} .0468091
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0681413{col 27}{space 2} .0315538{col 38}{space 1}    2.16{col 47}{space 3}0.031{col 55}{space 4}  .006297{col 68}{space 3} .1299856
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.543153{col 27}{space 2}  2.55756{col 38}{space 1}    3.73{col 47}{space 3}0.000{col 55}{space 4} 4.530428{col 68}{space 3} 14.55588
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0429159{col 44} .0254602{col 58} .0134164{col 70} .1372776
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.012086{col 44} .2958868{col 58} .5706444{col 70} 1.795019
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.226831{col 44} .3807595{col 58} 4.531384{col 70}  6.02901
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 20
. // Drop respondents who rated themselves as upper or upper middle class (based on V238)
. eststo PF5: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V238 != 1 & V238 != 2 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-53879.285}  
Iteration 1:{space 3}log pseudolikelihood = {res:-53879.285}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    23,962
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       552
{txt}{col 63}avg{col 67}={col 69}{res}   1,089.2
{txt}{col 63}max{col 67}={col 69}{res}     1,721
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   310.47
{txt}Log pseudolikelihood = {res}-53879.285{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1053583{col 27}{space 2} .0255876{col 38}{space 1}   -4.12{col 47}{space 3}0.000{col 55}{space 4} -.155509{col 68}{space 3}-.0552076
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3762821{col 27}{space 2} .0700787{col 38}{space 1}   -5.37{col 47}{space 3}0.000{col 55}{space 4}-.5136338{col 68}{space 3}-.2389303
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1544859{col 27}{space 2} .0595092{col 38}{space 1}    2.60{col 47}{space 3}0.009{col 55}{space 4}   .03785{col 68}{space 3} .2711219
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0255385{col 27}{space 2} .0045667{col 38}{space 1}   -5.59{col 47}{space 3}0.000{col 55}{space 4}-.0344891{col 68}{space 3}-.0165879
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0309343{col 27}{space 2} .0053936{col 38}{space 1}    5.74{col 47}{space 3}0.000{col 55}{space 4}  .020363{col 68}{space 3} .0415057
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0018255{col 27}{space 2} .0636918{col 38}{space 1}   -0.03{col 47}{space 3}0.977{col 55}{space 4} -.126659{col 68}{space 3} .1230081
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0916632{col 27}{space 2} .0867894{col 38}{space 1}   -1.06{col 47}{space 3}0.291{col 55}{space 4}-.2617672{col 68}{space 3} .0784409
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1660798{col 27}{space 2} .0339349{col 38}{space 1}    4.89{col 47}{space 3}0.000{col 55}{space 4} .0995686{col 68}{space 3} .2325911
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0491775{col 27}{space 2}  .051679{col 38}{space 1}    0.95{col 47}{space 3}0.341{col 55}{space 4}-.0521115{col 68}{space 3} .1504665
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5867335{col 27}{space 2} .2548258{col 38}{space 1}   -2.30{col 47}{space 3}0.021{col 55}{space 4}-1.086183{col 68}{space 3}-.0872841
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0136313{col 27}{space 2} .0230465{col 38}{space 1}   -0.59{col 47}{space 3}0.554{col 55}{space 4}-.0588015{col 68}{space 3} .0315389
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}  .020543{col 27}{space 2} .0411488{col 38}{space 1}    0.50{col 47}{space 3}0.618{col 55}{space 4}-.0601072{col 68}{space 3} .1011933
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0425516{col 27}{space 2}  .032825{col 38}{space 1}    1.30{col 47}{space 3}0.195{col 55}{space 4}-.0217842{col 68}{space 3} .1068875
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.307467{col 27}{space 2} 2.094848{col 38}{space 1}    3.49{col 47}{space 3}0.000{col 55}{space 4} 3.201641{col 68}{space 3} 11.41329
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0541202{col 44} .0218015{col 58} .0245734{col 70} .1191936
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.043877{col 44} .2004814{col 58} .7164272{col 70}  1.52099
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.225725{col 44} .3622828{col 58} 4.561792{col 70} 5.986288
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo PF6: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V238 != 1 & V238 != 2 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-53879.032}  
Iteration 1:{space 3}log pseudolikelihood = {res:-53879.032}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    23,962
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       552
{txt}{col 63}avg{col 67}={col 69}{res}   1,089.2
{txt}{col 63}max{col 67}={col 69}{res}     1,721
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   367.28
{txt}Log pseudolikelihood = {res}-53879.032{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0699072{col 27}{space 2} .0121593{col 38}{space 1}   -5.75{col 47}{space 3}0.000{col 55}{space 4}-.0937389{col 68}{space 3}-.0460755
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3766328{col 27}{space 2} .0701003{col 38}{space 1}   -5.37{col 47}{space 3}0.000{col 55}{space 4}-.5140269{col 68}{space 3}-.2392387
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1542948{col 27}{space 2} .0594957{col 38}{space 1}    2.59{col 47}{space 3}0.010{col 55}{space 4} .0376854{col 68}{space 3} .2709042
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0255497{col 27}{space 2} .0045723{col 38}{space 1}   -5.59{col 47}{space 3}0.000{col 55}{space 4}-.0345112{col 68}{space 3}-.0165881
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0309415{col 27}{space 2} .0053966{col 38}{space 1}    5.73{col 47}{space 3}0.000{col 55}{space 4} .0203643{col 68}{space 3} .0415186
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0020552{col 27}{space 2} .0637642{col 38}{space 1}   -0.03{col 47}{space 3}0.974{col 55}{space 4}-.1270307{col 68}{space 3} .1229204
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0923274{col 27}{space 2} .0867576{col 38}{space 1}   -1.06{col 47}{space 3}0.287{col 55}{space 4}-.2623692{col 68}{space 3} .0777144
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1661237{col 27}{space 2} .0338998{col 38}{space 1}    4.90{col 47}{space 3}0.000{col 55}{space 4} .0996813{col 68}{space 3} .2325661
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0490429{col 27}{space 2} .0516209{col 38}{space 1}    0.95{col 47}{space 3}0.342{col 55}{space 4}-.0521323{col 68}{space 3}  .150218
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7758556{col 27}{space 2} .2930424{col 38}{space 1}   -2.65{col 47}{space 3}0.008{col 55}{space 4}-1.350208{col 68}{space 3} -.201503
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0475052{col 27}{space 2} .0285928{col 38}{space 1}   -1.66{col 47}{space 3}0.097{col 55}{space 4} -.103546{col 68}{space 3} .0085356
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0312694{col 27}{space 2} .0391438{col 38}{space 1}   -0.80{col 47}{space 3}0.424{col 55}{space 4}-.1079898{col 68}{space 3}  .045451
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0690809{col 27}{space 2} .0316674{col 38}{space 1}    2.18{col 47}{space 3}0.029{col 55}{space 4}  .007014{col 68}{space 3} .1311479
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  9.74975{col 27}{space 2}   2.5616{col 38}{space 1}    3.81{col 47}{space 3}0.000{col 55}{space 4} 4.729106{col 68}{space 3} 14.77039
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0546365{col 44} .0216914{col 58} .0250926{col 70} .1189656
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.019959{col 44} .3004582{col 58} .5725808{col 70}  1.81689
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.225703{col 44}  .362275{col 58} 4.561783{col 70} 5.986249
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 21
. // Drop respondents who worried about government monitoring (based on V186)
. eststo PF3: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V186 != 1 & V186 != 2 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-38780.604}  
Iteration 1:{space 3}log pseudolikelihood = {res:-38780.604}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    17,302
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       225
{txt}{col 63}avg{col 67}={col 69}{res}     786.5
{txt}{col 63}max{col 67}={col 69}{res}     1,297
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   357.43
{txt}Log pseudolikelihood = {res}-38780.604{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1201492{col 27}{space 2} .0232077{col 38}{space 1}   -5.18{col 47}{space 3}0.000{col 55}{space 4}-.1656354{col 68}{space 3}-.0746631
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3877506{col 27}{space 2} .0740163{col 38}{space 1}   -5.24{col 47}{space 3}0.000{col 55}{space 4}-.5328199{col 68}{space 3}-.2426813
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1686169{col 27}{space 2} .0469895{col 38}{space 1}    3.59{col 47}{space 3}0.000{col 55}{space 4} .0765192{col 68}{space 3} .2607145
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0152817{col 27}{space 2} .0056755{col 38}{space 1}   -2.69{col 47}{space 3}0.007{col 55}{space 4}-.0264055{col 68}{space 3}-.0041578
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0182868{col 27}{space 2}   .00613{col 38}{space 1}    2.98{col 47}{space 3}0.003{col 55}{space 4} .0062721{col 68}{space 3} .0303015
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0667874{col 27}{space 2} .0507331{col 38}{space 1}    1.32{col 47}{space 3}0.188{col 55}{space 4}-.0326477{col 68}{space 3} .1662224
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0048434{col 27}{space 2} .1048557{col 38}{space 1}   -0.05{col 47}{space 3}0.963{col 55}{space 4}-.2103567{col 68}{space 3}   .20067
{txt}{space 7}income {c |}{col 15}{res}{space 2}  .154622{col 27}{space 2} .0335424{col 38}{space 1}    4.61{col 47}{space 3}0.000{col 55}{space 4} .0888801{col 68}{space 3} .2203638
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0042163{col 27}{space 2} .0474938{col 38}{space 1}    0.09{col 47}{space 3}0.929{col 55}{space 4}-.0888699{col 68}{space 3} .0973024
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5811715{col 27}{space 2} .2430101{col 38}{space 1}   -2.39{col 47}{space 3}0.017{col 55}{space 4}-1.057463{col 68}{space 3}-.1048804
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0041141{col 27}{space 2}  .021214{col 38}{space 1}   -0.19{col 47}{space 3}0.846{col 55}{space 4}-.0456928{col 68}{space 3} .0374646
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0266998{col 27}{space 2} .0398415{col 38}{space 1}    0.67{col 47}{space 3}0.503{col 55}{space 4}-.0513881{col 68}{space 3} .1047878
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0366242{col 27}{space 2} .0307243{col 38}{space 1}    1.19{col 47}{space 3}0.233{col 55}{space 4}-.0235944{col 68}{space 3} .0968428
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.550549{col 27}{space 2} 2.006193{col 38}{space 1}    3.76{col 47}{space 3}0.000{col 55}{space 4} 3.618483{col 68}{space 3} 11.48262
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0387339{col 44} .0216237{col 58} .0129687{col 70} .1156872
{txt}{space 18}var(_cons) {c |}{res}{col 33} .8942728{col 44}   .18934{col 58}   .59054{col 70} 1.354225
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.145042{col 44} .3910537{col 58} 4.432947{col 70} 5.971525
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo PF4: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V186 != 1 & V186 != 2 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-38781.435}  
Iteration 1:{space 3}log pseudolikelihood = {res:-38781.435}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    17,302
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       225
{txt}{col 63}avg{col 67}={col 69}{res}     786.5
{txt}{col 63}max{col 67}={col 69}{res}     1,297
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   384.47
{txt}Log pseudolikelihood = {res}-38781.435{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0756471{col 27}{space 2} .0112262{col 38}{space 1}   -6.74{col 47}{space 3}0.000{col 55}{space 4}  -.09765{col 68}{space 3}-.0536441
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3881481{col 27}{space 2} .0740296{col 38}{space 1}   -5.24{col 47}{space 3}0.000{col 55}{space 4}-.5332434{col 68}{space 3}-.2430528
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1686078{col 27}{space 2} .0469122{col 38}{space 1}    3.59{col 47}{space 3}0.000{col 55}{space 4} .0766615{col 68}{space 3} .2605541
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0153052{col 27}{space 2} .0056821{col 38}{space 1}   -2.69{col 47}{space 3}0.007{col 55}{space 4}-.0264418{col 68}{space 3}-.0041686
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0183115{col 27}{space 2} .0061333{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0062905{col 68}{space 3} .0303325
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0663045{col 27}{space 2} .0509344{col 38}{space 1}    1.30{col 47}{space 3}0.193{col 55}{space 4}-.0335251{col 68}{space 3} .1661342
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0060302{col 27}{space 2} .1048826{col 38}{space 1}   -0.06{col 47}{space 3}0.954{col 55}{space 4}-.2115964{col 68}{space 3}  .199536
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1547001{col 27}{space 2} .0334799{col 38}{space 1}    4.62{col 47}{space 3}0.000{col 55}{space 4} .0890808{col 68}{space 3} .2203195
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0041059{col 27}{space 2}  .047447{col 38}{space 1}    0.09{col 47}{space 3}0.931{col 55}{space 4}-.0888884{col 68}{space 3} .0971003
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7687862{col 27}{space 2} .2877533{col 38}{space 1}   -2.67{col 47}{space 3}0.008{col 55}{space 4}-1.332772{col 68}{space 3}   -.2048
{txt}growth_one_yr {c |}{col 15}{res}{space 2} -.039694{col 27}{space 2} .0288011{col 38}{space 1}   -1.38{col 47}{space 3}0.168{col 55}{space 4}-.0961432{col 68}{space 3} .0167552
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0330722{col 27}{space 2} .0411966{col 38}{space 1}   -0.80{col 47}{space 3}0.422{col 55}{space 4} -.113816{col 68}{space 3} .0476716
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0674845{col 27}{space 2} .0330432{col 38}{space 1}    2.04{col 47}{space 3}0.041{col 55}{space 4} .0027211{col 68}{space 3} .1322479
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.911093{col 27}{space 2} 2.475835{col 38}{space 1}    4.00{col 47}{space 3}0.000{col 55}{space 4} 5.058546{col 68}{space 3} 14.76364
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0389705{col 44} .0215046{col 58} .0132138{col 70}  .114933
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9657891{col 44} .2991979{col 58} .5262393{col 70}  1.77248
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.145022{col 44} .3910453{col 58} 4.432941{col 70} 5.971486
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 22
. // Drop respondents who had other people around who could follow the interview (based on V252)
. eststo PF7: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V252 != 2 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-47099.799}  
Iteration 1:{space 3}log pseudolikelihood = {res:-47099.799}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    20,997
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       465
{txt}{col 63}avg{col 67}={col 69}{res}     954.4
{txt}{col 63}max{col 67}={col 69}{res}     1,919
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   287.68
{txt}Log pseudolikelihood = {res}-47099.799{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1068266{col 27}{space 2} .0253446{col 38}{space 1}   -4.21{col 47}{space 3}0.000{col 55}{space 4} -.156501{col 68}{space 3}-.0571522
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3737943{col 27}{space 2} .0638934{col 38}{space 1}   -5.85{col 47}{space 3}0.000{col 55}{space 4} -.499023{col 68}{space 3}-.2485655
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1585795{col 27}{space 2}  .063767{col 38}{space 1}    2.49{col 47}{space 3}0.013{col 55}{space 4} .0335985{col 68}{space 3} .2835606
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0230115{col 27}{space 2} .0059419{col 38}{space 1}   -3.87{col 47}{space 3}0.000{col 55}{space 4}-.0346573{col 68}{space 3}-.0113657
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2}  .026849{col 27}{space 2} .0066944{col 38}{space 1}    4.01{col 47}{space 3}0.000{col 55}{space 4} .0137281{col 68}{space 3} .0399698
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0599334{col 27}{space 2} .0622085{col 38}{space 1}    0.96{col 47}{space 3}0.335{col 55}{space 4} -.061993{col 68}{space 3} .1818599
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1146086{col 27}{space 2} .0960832{col 38}{space 1}   -1.19{col 47}{space 3}0.233{col 55}{space 4}-.3029282{col 68}{space 3}  .073711
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1816168{col 27}{space 2} .0307233{col 38}{space 1}    5.91{col 47}{space 3}0.000{col 55}{space 4} .1214001{col 68}{space 3} .2418334
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0222591{col 27}{space 2} .0387401{col 38}{space 1}   -0.57{col 47}{space 3}0.566{col 55}{space 4}-.0981882{col 68}{space 3} .0536701
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6157654{col 27}{space 2} .2569351{col 38}{space 1}   -2.40{col 47}{space 3}0.017{col 55}{space 4}-1.119349{col 68}{space 3}-.1121818
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0166974{col 27}{space 2} .0227639{col 38}{space 1}   -0.73{col 47}{space 3}0.463{col 55}{space 4}-.0613139{col 68}{space 3}  .027919
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}  .019364{col 27}{space 2} .0409627{col 38}{space 1}    0.47{col 47}{space 3}0.636{col 55}{space 4}-.0609214{col 68}{space 3} .0996494
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0443325{col 27}{space 2} .0327129{col 38}{space 1}    1.36{col 47}{space 3}0.175{col 55}{space 4}-.0197836{col 68}{space 3} .1084486
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  7.54933{col 27}{space 2}  2.08393{col 38}{space 1}    3.62{col 47}{space 3}0.000{col 55}{space 4} 3.464901{col 68}{space 3} 11.63376
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0464612{col 44} .0245355{col 58} .0165037{col 70}  .130797
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9656189{col 44} .1935246{col 58} .6519468{col 70} 1.430209
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.167107{col 44} .4385106{col 58} 4.375318{col 70} 6.102185
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo PF8: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if V252 != 2 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-47100.146}  
Iteration 1:{space 3}log pseudolikelihood = {res:-47100.146}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    20,997
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       465
{txt}{col 63}avg{col 67}={col 69}{res}     954.4
{txt}{col 63}max{col 67}={col 69}{res}     1,919
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   310.95
{txt}Log pseudolikelihood = {res}-47100.146{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0684166{col 27}{space 2} .0120167{col 38}{space 1}   -5.69{col 47}{space 3}0.000{col 55}{space 4}-.0919689{col 68}{space 3}-.0448643
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3744391{col 27}{space 2} .0639192{col 38}{space 1}   -5.86{col 47}{space 3}0.000{col 55}{space 4}-.4997183{col 68}{space 3}-.2491599
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1584448{col 27}{space 2}  .063793{col 38}{space 1}    2.48{col 47}{space 3}0.013{col 55}{space 4} .0334128{col 68}{space 3} .2834767
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0230243{col 27}{space 2} .0059464{col 38}{space 1}   -3.87{col 47}{space 3}0.000{col 55}{space 4} -.034679{col 68}{space 3}-.0113696
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0268578{col 27}{space 2} .0066984{col 38}{space 1}    4.01{col 47}{space 3}0.000{col 55}{space 4} .0137291{col 68}{space 3} .0399865
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0596472{col 27}{space 2} .0623446{col 38}{space 1}    0.96{col 47}{space 3}0.339{col 55}{space 4} -.062546{col 68}{space 3} .1818404
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1153336{col 27}{space 2} .0960993{col 38}{space 1}   -1.20{col 47}{space 3}0.230{col 55}{space 4}-.3036848{col 68}{space 3} .0730175
{txt}{space 7}income {c |}{col 15}{res}{space 2}  .181684{col 27}{space 2} .0306948{col 38}{space 1}    5.92{col 47}{space 3}0.000{col 55}{space 4} .1215232{col 68}{space 3} .2418447
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0222748{col 27}{space 2} .0386959{col 38}{space 1}   -0.58{col 47}{space 3}0.565{col 55}{space 4}-.0981173{col 68}{space 3} .0535678
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.7904323{col 27}{space 2} .2975241{col 38}{space 1}   -2.66{col 47}{space 3}0.008{col 55}{space 4}-1.373569{col 68}{space 3}-.2072958
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0491867{col 27}{space 2}  .028479{col 38}{space 1}   -1.73{col 47}{space 3}0.084{col 55}{space 4}-.1050045{col 68}{space 3} .0066311
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0335956{col 27}{space 2} .0397925{col 38}{space 1}   -0.84{col 47}{space 3}0.399{col 55}{space 4}-.1115875{col 68}{space 3} .0443962
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0715718{col 27}{space 2} .0328128{col 38}{space 1}    2.18{col 47}{space 3}0.029{col 55}{space 4} .0072599{col 68}{space 3} .1358836
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.768524{col 27}{space 2} 2.571058{col 38}{space 1}    3.80{col 47}{space 3}0.000{col 55}{space 4} 4.729343{col 68}{space 3} 14.80771
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0467224{col 44} .0246848{col 58} .0165887{col 70} .1315948
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9967602{col 44} .2977436{col 58} .5550436{col 70} 1.790005
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.167092{col 44} .4385137{col 58} 4.375298{col 70} 6.102177
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 23
. // Drop most repressive regimes (based on political terror scale)
. eststo PF9: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if PTS != 5 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-61162.418}  
Iteration 1:{space 3}log pseudolikelihood = {res:-61162.418}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    27,192
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        19
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       946
{txt}{col 63}avg{col 67}={col 69}{res}   1,431.2
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   798.14
{txt}Log pseudolikelihood = {res}-61162.418{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:19} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1063995{col 27}{space 2} .0291575{col 38}{space 1}   -3.65{col 47}{space 3}0.000{col 55}{space 4}-.1635473{col 68}{space 3}-.0492518
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3824945{col 27}{space 2} .0553272{col 38}{space 1}   -6.91{col 47}{space 3}0.000{col 55}{space 4}-.4909338{col 68}{space 3}-.2740553
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1367389{col 27}{space 2} .0451789{col 38}{space 1}    3.03{col 47}{space 3}0.002{col 55}{space 4}   .04819{col 68}{space 3} .2252879
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0188537{col 27}{space 2} .0042802{col 38}{space 1}   -4.40{col 47}{space 3}0.000{col 55}{space 4}-.0272427{col 68}{space 3}-.0104647
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0242335{col 27}{space 2} .0051132{col 38}{space 1}    4.74{col 47}{space 3}0.000{col 55}{space 4} .0142119{col 68}{space 3} .0342552
{txt}{space 6}married {c |}{col 15}{res}{space 2}  .025564{col 27}{space 2} .0601794{col 38}{space 1}    0.42{col 47}{space 3}0.671{col 55}{space 4}-.0923855{col 68}{space 3} .1435134
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1072999{col 27}{space 2} .0624861{col 38}{space 1}   -1.72{col 47}{space 3}0.086{col 55}{space 4}-.2297705{col 68}{space 3} .0151707
{txt}{space 7}income {c |}{col 15}{res}{space 2}  .167542{col 27}{space 2} .0344699{col 38}{space 1}    4.86{col 47}{space 3}0.000{col 55}{space 4} .0999823{col 68}{space 3} .2351016
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0266124{col 27}{space 2} .0415777{col 38}{space 1}    0.64{col 47}{space 3}0.522{col 55}{space 4}-.0548783{col 68}{space 3} .1081031
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5495591{col 27}{space 2} .2928435{col 38}{space 1}   -1.88{col 47}{space 3}0.061{col 55}{space 4}-1.123522{col 68}{space 3} .0244035
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0098317{col 27}{space 2} .0255337{col 38}{space 1}   -0.39{col 47}{space 3}0.700{col 55}{space 4}-.0598768{col 68}{space 3} .0402134
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0194949{col 27}{space 2} .0474444{col 38}{space 1}    0.41{col 47}{space 3}0.681{col 55}{space 4}-.0734943{col 68}{space 3} .1124842
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0393409{col 27}{space 2} .0364674{col 38}{space 1}    1.08{col 47}{space 3}0.281{col 55}{space 4}-.0321339{col 68}{space 3} .1108156
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.067097{col 27}{space 2}  2.35454{col 38}{space 1}    3.00{col 47}{space 3}0.003{col 55}{space 4} 2.452284{col 68}{space 3} 11.68191
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0351077{col 44} .0197818{col 58} .0116355{col 70} .1059304
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.111511{col 44} .2055004{col 58} .7736423{col 70} 1.596934
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.23907{col 44} .4526343{col 58} 4.422969{col 70} 6.205754
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo PF10: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov if PTS != 5 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -61161.76}  
Iteration 1:{space 3}log pseudolikelihood = {res: -61161.76}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    27,192
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        19
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       946
{txt}{col 63}avg{col 67}={col 69}{res}   1,431.2
{txt}{col 63}max{col 67}={col 69}{res}     1,997
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   449.37
{txt}Log pseudolikelihood = {res} -61161.76{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:19} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0783911{col 27}{space 2} .0150872{col 38}{space 1}   -5.20{col 47}{space 3}0.000{col 55}{space 4}-.1079615{col 68}{space 3}-.0488207
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3827505{col 27}{space 2} .0553172{col 38}{space 1}   -6.92{col 47}{space 3}0.000{col 55}{space 4}-.4911703{col 68}{space 3}-.2743308
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1366453{col 27}{space 2}  .045154{col 38}{space 1}    3.03{col 47}{space 3}0.002{col 55}{space 4} .0481451{col 68}{space 3} .2251455
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0188662{col 27}{space 2} .0042809{col 38}{space 1}   -4.41{col 47}{space 3}0.000{col 55}{space 4}-.0272567{col 68}{space 3}-.0104758
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0242442{col 27}{space 2} .0051128{col 38}{space 1}    4.74{col 47}{space 3}0.000{col 55}{space 4} .0142234{col 68}{space 3} .0342651
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0253127{col 27}{space 2} .0602218{col 38}{space 1}    0.42{col 47}{space 3}0.674{col 55}{space 4}-.0927199{col 68}{space 3} .1433452
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1077197{col 27}{space 2} .0624691{col 38}{space 1}   -1.72{col 47}{space 3}0.085{col 55}{space 4}-.2301568{col 68}{space 3} .0147175
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1675593{col 27}{space 2}   .03444{col 38}{space 1}    4.87{col 47}{space 3}0.000{col 55}{space 4} .1000581{col 68}{space 3} .2350606
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0265659{col 27}{space 2} .0415465{col 38}{space 1}    0.64{col 47}{space 3}0.523{col 55}{space 4}-.0548638{col 68}{space 3} .1079956
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2} -.803995{col 27}{space 2} .3491493{col 38}{space 1}   -2.30{col 47}{space 3}0.021{col 55}{space 4}-1.488315{col 68}{space 3}-.1196749
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0512329{col 27}{space 2} .0317575{col 38}{space 1}   -1.61{col 47}{space 3}0.107{col 55}{space 4}-.1134765{col 68}{space 3} .0110106
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0259218{col 27}{space 2} .0418471{col 38}{space 1}   -0.62{col 47}{space 3}0.536{col 55}{space 4}-.1079405{col 68}{space 3}  .056097
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2}  .061337{col 27}{space 2} .0325179{col 38}{space 1}    1.89{col 47}{space 3}0.059{col 55}{space 4}-.0023969{col 68}{space 3} .1250709
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 10.34247{col 27}{space 2} 2.969607{col 38}{space 1}    3.48{col 47}{space 3}0.000{col 55}{space 4} 4.522148{col 68}{space 3}  16.1628
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0352977{col 44} .0198761{col 58} .0117068{col 70} .1064282
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.036742{col 44} .3273537{col 58} .5583437{col 70} 1.925039
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  5.23906{col 44} .4526342{col 58} 4.422959{col 70} 6.205744
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 24
. /* Drop respondents whose conceptions of democracy are different from expert coders' */
. // Preferred specification (discussed in the main text)
. eststo COD1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if V131 < 10 & V132 < 10 & V133 > 5 & V134 < 10 & ///
>         V135 < 10 & V137 < 10 & V138 < 10 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-24871.101}  
Iteration 1:{space 3}log pseudolikelihood = {res:-24871.101}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    11,517
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       148
{txt}{col 63}avg{col 67}={col 69}{res}     523.5
{txt}{col 63}max{col 67}={col 69}{res}     1,165
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   235.48
{txt}Log pseudolikelihood = {res}-24871.101{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0989374{col 27}{space 2} .0265125{col 38}{space 1}   -3.73{col 47}{space 3}0.000{col 55}{space 4}-.1509011{col 68}{space 3}-.0469738
{txt}{space 3}university {c |}{col 15}{res}{space 2} -.370172{col 27}{space 2} .0860177{col 38}{space 1}   -4.30{col 47}{space 3}0.000{col 55}{space 4}-.5387635{col 68}{space 3}-.2015805
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1507896{col 27}{space 2} .0422353{col 38}{space 1}    3.57{col 47}{space 3}0.000{col 55}{space 4}   .06801{col 68}{space 3} .2335692
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0110238{col 27}{space 2} .0075808{col 38}{space 1}   -1.45{col 47}{space 3}0.146{col 55}{space 4}-.0258819{col 68}{space 3} .0038342
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0163054{col 27}{space 2}  .009292{col 38}{space 1}    1.75{col 47}{space 3}0.079{col 55}{space 4}-.0019065{col 68}{space 3} .0345173
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0109468{col 27}{space 2} .0832441{col 38}{space 1}   -0.13{col 47}{space 3}0.895{col 55}{space 4}-.1741022{col 68}{space 3} .1522086
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0494537{col 27}{space 2} .1311304{col 38}{space 1}   -0.38{col 47}{space 3}0.706{col 55}{space 4}-.3064645{col 68}{space 3} .2075571
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1535451{col 27}{space 2} .0316908{col 38}{space 1}    4.85{col 47}{space 3}0.000{col 55}{space 4} .0914322{col 68}{space 3}  .215658
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0006216{col 27}{space 2} .0586548{col 38}{space 1}    0.01{col 47}{space 3}0.992{col 55}{space 4}-.1143397{col 68}{space 3} .1155829
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5281102{col 27}{space 2} .2487698{col 38}{space 1}   -2.12{col 47}{space 3}0.034{col 55}{space 4} -1.01569{col 68}{space 3}-.0405303
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0000459{col 27}{space 2} .0228545{col 38}{space 1}    0.00{col 47}{space 3}0.998{col 55}{space 4}-.0447482{col 68}{space 3}   .04484
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0467707{col 27}{space 2} .0390477{col 38}{space 1}    1.20{col 47}{space 3}0.231{col 55}{space 4}-.0297613{col 68}{space 3} .1233027
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0187864{col 27}{space 2} .0336114{col 38}{space 1}    0.56{col 47}{space 3}0.576{col 55}{space 4}-.0470908{col 68}{space 3} .0846636
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.402614{col 27}{space 2}  1.99694{col 38}{space 1}    3.21{col 47}{space 3}0.001{col 55}{space 4} 2.488682{col 68}{space 3} 10.31654
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0499036{col 44} .0368935{col 58} .0117177{col 70} .2125302
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9063672{col 44} .2055034{col 58} .5811774{col 70} 1.413513
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  4.35492{col 44} .3817595{col 58} 3.667435{col 70} 5.171278
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo COD2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if V131 < 10 & V132 < 10 & V133 > 5 & V134 < 10 & ///
>         V135 < 10 & V137 < 10 & V138 < 10 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-24871.489}  
Iteration 1:{space 3}log pseudolikelihood = {res:-24871.489}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}    11,517
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       148
{txt}{col 63}avg{col 67}={col 69}{res}     523.5
{txt}{col 63}max{col 67}={col 69}{res}     1,165
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   376.86
{txt}Log pseudolikelihood = {res}-24871.489{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0631653{col 27}{space 2} .0122367{col 38}{space 1}   -5.16{col 47}{space 3}0.000{col 55}{space 4}-.0871488{col 68}{space 3}-.0391817
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3711071{col 27}{space 2} .0859328{col 38}{space 1}   -4.32{col 47}{space 3}0.000{col 55}{space 4}-.5395323{col 68}{space 3} -.202682
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1506674{col 27}{space 2} .0422862{col 38}{space 1}    3.56{col 47}{space 3}0.000{col 55}{space 4} .0677881{col 68}{space 3} .2335467
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0110629{col 27}{space 2} .0075853{col 38}{space 1}   -1.46{col 47}{space 3}0.145{col 55}{space 4}-.0259298{col 68}{space 3}  .003804
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0163339{col 27}{space 2}  .009295{col 38}{space 1}    1.76{col 47}{space 3}0.079{col 55}{space 4}-.0018839{col 68}{space 3} .0345517
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0112989{col 27}{space 2} .0834656{col 38}{space 1}   -0.14{col 47}{space 3}0.892{col 55}{space 4}-.1748885{col 68}{space 3} .1522907
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0504013{col 27}{space 2}  .131076{col 38}{space 1}   -0.38{col 47}{space 3}0.701{col 55}{space 4}-.3073055{col 68}{space 3} .2065029
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1537778{col 27}{space 2} .0316393{col 38}{space 1}    4.86{col 47}{space 3}0.000{col 55}{space 4}  .091766{col 68}{space 3} .2157896
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0005453{col 27}{space 2} .0586254{col 38}{space 1}    0.01{col 47}{space 3}0.993{col 55}{space 4}-.1143585{col 68}{space 3}  .115449
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6877527{col 27}{space 2} .2877323{col 38}{space 1}   -2.39{col 47}{space 3}0.017{col 55}{space 4}-1.251698{col 68}{space 3}-.1238078
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0299305{col 27}{space 2} .0276534{col 38}{space 1}   -1.08{col 47}{space 3}0.279{col 55}{space 4}-.0841303{col 68}{space 3} .0242692
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0023769{col 27}{space 2} .0361649{col 38}{space 1}   -0.07{col 47}{space 3}0.948{col 55}{space 4}-.0732588{col 68}{space 3} .0685049
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0440477{col 27}{space 2} .0315453{col 38}{space 1}    1.40{col 47}{space 3}0.163{col 55}{space 4}-.0177799{col 68}{space 3} .1058754
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 8.433196{col 27}{space 2}  2.49925{col 38}{space 1}    3.37{col 47}{space 3}0.001{col 55}{space 4} 3.534756{col 68}{space 3} 13.33164
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0499125{col 44} .0371207{col 58} .0116187{col 70} .2144177
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9385474{col 44} .2954011{col 58} .5064617{col 70} 1.739265
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.354926{col 44} .3817526{col 58} 3.667453{col 70} 5.171268
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 25
. // Ease the restriction from "10" to "9"
. eststo COD3: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if V131 < 9 & V132 < 9 & V133 > 5 & V134 < 9 & ///
>         V135 < 9 & V137 < 9 & V138 < 9 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-15087.494}  
Iteration 1:{space 3}log pseudolikelihood = {res:-15087.492}  
Iteration 2:{space 3}log pseudolikelihood = {res:-15087.492}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     7,039
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        81
{txt}{col 63}avg{col 67}={col 69}{res}     320.0
{txt}{col 63}max{col 67}={col 69}{res}       957
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   225.44
{txt}Log pseudolikelihood = {res}-15087.492{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2} -.093218{col 27}{space 2}  .028054{col 38}{space 1}   -3.32{col 47}{space 3}0.001{col 55}{space 4}-.1482028{col 68}{space 3}-.0382332
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.2644103{col 27}{space 2} .0939803{col 38}{space 1}   -2.81{col 47}{space 3}0.005{col 55}{space 4}-.4486083{col 68}{space 3}-.0802123
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1680896{col 27}{space 2} .0561293{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0580781{col 68}{space 3}  .278101
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.013516{col 27}{space 2} .0096435{col 38}{space 1}   -1.40{col 47}{space 3}0.161{col 55}{space 4} -.032417{col 68}{space 3}  .005385
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0226665{col 27}{space 2} .0111286{col 38}{space 1}    2.04{col 47}{space 3}0.042{col 55}{space 4} .0008548{col 68}{space 3} .0444783
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0352556{col 27}{space 2} .0847572{col 38}{space 1}   -0.42{col 47}{space 3}0.677{col 55}{space 4}-.2013768{col 68}{space 3} .1308655
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} .0111283{col 27}{space 2} .1651117{col 38}{space 1}    0.07{col 47}{space 3}0.946{col 55}{space 4}-.3124847{col 68}{space 3} .3347412
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1492874{col 27}{space 2} .0379157{col 38}{space 1}    3.94{col 47}{space 3}0.000{col 55}{space 4}  .074974{col 68}{space 3} .2236008
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0126074{col 27}{space 2} .0823909{col 38}{space 1}   -0.15{col 47}{space 3}0.878{col 55}{space 4}-.1740905{col 68}{space 3} .1488757
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.4426651{col 27}{space 2} .2502011{col 38}{space 1}   -1.77{col 47}{space 3}0.077{col 55}{space 4}-.9330501{col 68}{space 3}   .04772
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0106101{col 27}{space 2} .0238427{col 38}{space 1}    0.45{col 47}{space 3}0.656{col 55}{space 4}-.0361206{col 68}{space 3} .0573409
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0495823{col 27}{space 2} .0388674{col 38}{space 1}    1.28{col 47}{space 3}0.202{col 55}{space 4}-.0265964{col 68}{space 3} .1257609
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0120067{col 27}{space 2} .0346545{col 38}{space 1}    0.35{col 47}{space 3}0.729{col 55}{space 4}-.0559148{col 68}{space 3} .0799282
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 5.539102{col 27}{space 2} 1.983456{col 38}{space 1}    2.79{col 47}{space 3}0.005{col 55}{space 4} 1.651599{col 68}{space 3} 9.426605
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0416644{col 44} .0353355{col 58} .0079043{col 70} .2196179
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9157836{col 44} .2114813{col 58} .5824055{col 70} 1.439993
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.199973{col 44} .4090739{col 58} 3.470086{col 70} 5.083383
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo COD4: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if V131 < 9 & V132 < 9 & V133 > 5 & V134 < 9 & ///
>         V135 < 9 & V137 < 9 & V138 < 9 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-15087.357}  
Iteration 1:{space 3}log pseudolikelihood = {res:-15087.355}  
Iteration 2:{space 3}log pseudolikelihood = {res:-15087.355}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     7,039
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        81
{txt}{col 63}avg{col 67}={col 69}{res}     320.0
{txt}{col 63}max{col 67}={col 69}{res}       957
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   189.91
{txt}Log pseudolikelihood = {res}-15087.355{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0617568{col 27}{space 2} .0125961{col 38}{space 1}   -4.90{col 47}{space 3}0.000{col 55}{space 4}-.0864448{col 68}{space 3}-.0370689
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.2661346{col 27}{space 2} .0939649{col 38}{space 1}   -2.83{col 47}{space 3}0.005{col 55}{space 4}-.4503024{col 68}{space 3}-.0819668
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1681531{col 27}{space 2}  .056279{col 38}{space 1}    2.99{col 47}{space 3}0.003{col 55}{space 4} .0578483{col 68}{space 3} .2784578
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.013575{col 27}{space 2} .0096511{col 38}{space 1}   -1.41{col 47}{space 3}0.160{col 55}{space 4}-.0324909{col 68}{space 3} .0053408
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0226961{col 27}{space 2}  .011139{col 38}{space 1}    2.04{col 47}{space 3}0.042{col 55}{space 4} .0008642{col 68}{space 3} .0445281
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.0352634{col 27}{space 2} .0850933{col 38}{space 1}   -0.41{col 47}{space 3}0.679{col 55}{space 4}-.2020433{col 68}{space 3} .1315165
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} .0093547{col 27}{space 2} .1648629{col 38}{space 1}    0.06{col 47}{space 3}0.955{col 55}{space 4}-.3137706{col 68}{space 3}   .33248
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1498066{col 27}{space 2} .0378942{col 38}{space 1}    3.95{col 47}{space 3}0.000{col 55}{space 4} .0755353{col 68}{space 3} .2240779
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0130405{col 27}{space 2} .0823248{col 38}{space 1}   -0.16{col 47}{space 3}0.874{col 55}{space 4}-.1743942{col 68}{space 3} .1483132
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6070005{col 27}{space 2} .2848512{col 38}{space 1}   -2.13{col 47}{space 3}0.033{col 55}{space 4}-1.165299{col 68}{space 3}-.0487024
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0193373{col 27}{space 2}  .027658{col 38}{space 1}   -0.70{col 47}{space 3}0.484{col 55}{space 4}-.0735461{col 68}{space 3} .0348715
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0035811{col 27}{space 2} .0337614{col 38}{space 1}    0.11{col 47}{space 3}0.916{col 55}{space 4}-.0625901{col 68}{space 3} .0697523
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0354921{col 27}{space 2}  .030799{col 38}{space 1}    1.15{col 47}{space 3}0.249{col 55}{space 4}-.0248729{col 68}{space 3} .0958571
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.675283{col 27}{space 2} 2.494968{col 38}{space 1}    3.08{col 47}{space 3}0.002{col 55}{space 4} 2.785236{col 68}{space 3} 12.56533
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33}  .042612{col 44} .0361786{col 58} .0080694{col 70} .2250208
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9028446{col 44} .2899757{col 58} .4810874{col 70} 1.694346
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  4.19991{col 44} .4090762{col 58}  3.47002{col 70} 5.083327
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 26
. // Further ease the restriction from "9" to "8"
. eststo COD5: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if V131 < 8 & V132 < 8 & V133 > 5 & V134 < 8 & ///
>         V135 < 8 & V137 < 8 & V138 < 8 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-7119.2638}  
Iteration 1:{space 3}log pseudolikelihood = {res: -7118.722}  
Iteration 2:{space 3}log pseudolikelihood = {res:-7118.7178}  
Iteration 3:{space 3}log pseudolikelihood = {res:-7118.7178}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     3,345
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        21
{txt}{col 63}avg{col 67}={col 69}{res}     152.0
{txt}{col 63}max{col 67}={col 69}{res}       477
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   263.53
{txt}Log pseudolikelihood = {res}-7118.7178{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.0967017{col 27}{space 2} .0299251{col 38}{space 1}   -3.23{col 47}{space 3}0.001{col 55}{space 4}-.1553537{col 68}{space 3}-.0380496
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.1854158{col 27}{space 2} .0868409{col 38}{space 1}   -2.14{col 47}{space 3}0.033{col 55}{space 4}-.3556208{col 68}{space 3}-.0152109
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1534595{col 27}{space 2} .0630611{col 38}{space 1}    2.43{col 47}{space 3}0.015{col 55}{space 4} .0298621{col 68}{space 3}  .277057
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.003856{col 27}{space 2}  .014171{col 38}{space 1}   -0.27{col 47}{space 3}0.786{col 55}{space 4}-.0316307{col 68}{space 3} .0239187
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0146385{col 27}{space 2}  .016211{col 38}{space 1}    0.90{col 47}{space 3}0.367{col 55}{space 4}-.0171346{col 68}{space 3} .0464115
{txt}{space 6}married {c |}{col 15}{res}{space 2} -.124376{col 27}{space 2} .1403855{col 38}{space 1}   -0.89{col 47}{space 3}0.376{col 55}{space 4}-.3995266{col 68}{space 3} .1507745
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0325488{col 27}{space 2} .1836699{col 38}{space 1}   -0.18{col 47}{space 3}0.859{col 55}{space 4}-.3925351{col 68}{space 3} .3274376
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1651733{col 27}{space 2} .0559562{col 38}{space 1}    2.95{col 47}{space 3}0.003{col 55}{space 4} .0555011{col 68}{space 3} .2748455
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0889117{col 27}{space 2} .0820238{col 38}{space 1}   -1.08{col 47}{space 3}0.278{col 55}{space 4}-.2496755{col 68}{space 3}  .071852
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.4067519{col 27}{space 2} .2576849{col 38}{space 1}   -1.58{col 47}{space 3}0.114{col 55}{space 4} -.911805{col 68}{space 3} .0983012
{txt}growth_one_yr {c |}{col 15}{res}{space 2} .0064572{col 27}{space 2} .0238982{col 38}{space 1}    0.27{col 47}{space 3}0.787{col 55}{space 4}-.0403823{col 68}{space 3} .0532968
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0416869{col 27}{space 2} .0374941{col 38}{space 1}    1.11{col 47}{space 3}0.266{col 55}{space 4}-.0318002{col 68}{space 3}  .115174
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0163979{col 27}{space 2} .0345351{col 38}{space 1}    0.47{col 47}{space 3}0.635{col 55}{space 4}-.0512897{col 68}{space 3} .0840854
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 5.115041{col 27}{space 2} 1.940071{col 38}{space 1}    2.64{col 47}{space 3}0.008{col 55}{space 4} 1.312571{col 68}{space 3} 8.917511
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} 1.98e-10{col 44} .0000343{col 58}        0{col 70}        .
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9947145{col 44} .2850473{col 58} .5672527{col 70} 1.744297
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.039419{col 44} .3732369{col 58} 3.370304{col 70} 4.841376
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo COD6: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if V131 < 8 & V132 < 8 & V133 > 5 & V134 < 8 & ///
>         V135 < 8 & V137 < 8 & V138 < 8 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-7119.0493}  
Iteration 1:{space 3}log pseudolikelihood = {res:-7118.5037}  
Iteration 2:{space 3}log pseudolikelihood = {res:-7118.4983}  
Iteration 3:{space 3}log pseudolikelihood = {res:-7118.4983}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     3,345
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        21
{txt}{col 63}avg{col 67}={col 69}{res}     152.0
{txt}{col 63}max{col 67}={col 69}{res}       477
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   335.28
{txt}Log pseudolikelihood = {res}-7118.4983{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0648006{col 27}{space 2} .0129199{col 38}{space 1}   -5.02{col 47}{space 3}0.000{col 55}{space 4}-.0901231{col 68}{space 3}-.0394781
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.1866084{col 27}{space 2} .0869531{col 38}{space 1}   -2.15{col 47}{space 3}0.032{col 55}{space 4}-.3570335{col 68}{space 3}-.0161834
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1542683{col 27}{space 2} .0632027{col 38}{space 1}    2.44{col 47}{space 3}0.015{col 55}{space 4} .0303933{col 68}{space 3} .2781433
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0039043{col 27}{space 2} .0141739{col 38}{space 1}   -0.28{col 47}{space 3}0.783{col 55}{space 4}-.0316846{col 68}{space 3} .0238761
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0146359{col 27}{space 2}  .016219{col 38}{space 1}    0.90{col 47}{space 3}0.367{col 55}{space 4}-.0171526{col 68}{space 3} .0464245
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.1244178{col 27}{space 2} .1411288{col 38}{space 1}   -0.88{col 47}{space 3}0.378{col 55}{space 4}-.4010251{col 68}{space 3} .1521895
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.0356756{col 27}{space 2} .1830535{col 38}{space 1}   -0.19{col 47}{space 3}0.845{col 55}{space 4}-.3944539{col 68}{space 3} .3231027
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1658204{col 27}{space 2} .0560397{col 38}{space 1}    2.96{col 47}{space 3}0.003{col 55}{space 4} .0559847{col 68}{space 3} .2756561
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0894768{col 27}{space 2} .0820843{col 38}{space 1}   -1.09{col 47}{space 3}0.276{col 55}{space 4} -.250359{col 68}{space 3} .0714055
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.5771653{col 27}{space 2} .2899237{col 38}{space 1}   -1.99{col 47}{space 3}0.047{col 55}{space 4}-1.145405{col 68}{space 3}-.0089252
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0250508{col 27}{space 2} .0279516{col 38}{space 1}   -0.90{col 47}{space 3}0.370{col 55}{space 4} -.079835{col 68}{space 3} .0297334
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0059187{col 27}{space 2} .0316632{col 38}{space 1}   -0.19{col 47}{space 3}0.852{col 55}{space 4}-.0679774{col 68}{space 3} .0561399
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0404901{col 27}{space 2} .0301944{col 38}{space 1}    1.34{col 47}{space 3}0.180{col 55}{space 4}-.0186898{col 68}{space 3}   .09967
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.365498{col 27}{space 2} 2.452499{col 38}{space 1}    3.00{col 47}{space 3}0.003{col 55}{space 4} 2.558688{col 68}{space 3} 12.17231
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} 4.08e-10{col 44} 3.50e-10{col 58} 7.60e-11{col 70} 2.19e-09
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9716381{col 44} .3611223{col 58} .4689689{col 70} 2.013098
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.039486{col 44} .3730949{col 58} 3.370601{col 70} 4.841107
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 27
. // Only include respondents who rate free elections (V133) as strictly higher
. // than all one of the other "strange" items (V131, V132, V134, V135, V137, V138)
. gen include1 = 0
{txt}
{com}. replace include1 = 1 if V133 > V131 & V133 > V132 & V133 > V134 ///
>         & V133 > V135 & V133 > V137 & V133 > V138
{txt}(4,771 real changes made)

{com}. eststo DRP1: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if include1 == 1 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-10033.174}  
Iteration 1:{space 3}log pseudolikelihood = {res:-10033.172}  
Iteration 2:{space 3}log pseudolikelihood = {res:-10033.172}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     4,556
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        45
{txt}{col 63}avg{col 67}={col 69}{res}     207.1
{txt}{col 63}max{col 67}={col 69}{res}       538
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   166.98
{txt}Log pseudolikelihood = {res}-10033.172{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2} -.110828{col 27}{space 2} .0257893{col 38}{space 1}   -4.30{col 47}{space 3}0.000{col 55}{space 4}-.1613741{col 68}{space 3}-.0602819
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4456015{col 27}{space 2} .1225185{col 38}{space 1}   -3.64{col 47}{space 3}0.000{col 55}{space 4}-.6857334{col 68}{space 3}-.2054696
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1885355{col 27}{space 2} .0883593{col 38}{space 1}    2.13{col 47}{space 3}0.033{col 55}{space 4} .0153544{col 68}{space 3} .3617166
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0233598{col 27}{space 2} .0162691{col 38}{space 1}   -1.44{col 47}{space 3}0.151{col 55}{space 4}-.0552467{col 68}{space 3}  .008527
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0303926{col 27}{space 2} .0185706{col 38}{space 1}    1.64{col 47}{space 3}0.102{col 55}{space 4}-.0060051{col 68}{space 3} .0667904
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0279533{col 27}{space 2} .0816583{col 38}{space 1}    0.34{col 47}{space 3}0.732{col 55}{space 4} -.132094{col 68}{space 3} .1880005
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}  .087176{col 27}{space 2} .1888539{col 38}{space 1}    0.46{col 47}{space 3}0.644{col 55}{space 4}-.2829708{col 68}{space 3} .4573228
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1897962{col 27}{space 2} .0434093{col 38}{space 1}    4.37{col 47}{space 3}0.000{col 55}{space 4} .1047156{col 68}{space 3} .2748768
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0868858{col 27}{space 2} .0790673{col 38}{space 1}   -1.10{col 47}{space 3}0.272{col 55}{space 4}-.2418548{col 68}{space 3} .0680832
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.8199918{col 27}{space 2}  .256209{col 38}{space 1}   -3.20{col 47}{space 3}0.001{col 55}{space 4}-1.322152{col 68}{space 3}-.3178314
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0046199{col 27}{space 2} .0239562{col 38}{space 1}   -0.19{col 47}{space 3}0.847{col 55}{space 4}-.0515731{col 68}{space 3} .0423333
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}  .043348{col 27}{space 2} .0369638{col 38}{space 1}    1.17{col 47}{space 3}0.241{col 55}{space 4}-.0290996{col 68}{space 3} .1157957
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0340316{col 27}{space 2} .0329306{col 38}{space 1}    1.03{col 47}{space 3}0.301{col 55}{space 4}-.0305113{col 68}{space 3} .0985744
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 8.972515{col 27}{space 2} 1.991313{col 38}{space 1}    4.51{col 47}{space 3}0.000{col 55}{space 4} 5.069613{col 68}{space 3} 12.87542
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0808211{col 44} .0648111{col 58} .0167857{col 70} .3891438
{txt}{space 18}var(_cons) {c |}{res}{col 33} .9405234{col 44} .2276635{col 58} .5852311{col 70} 1.511513
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.699699{col 44} .3611494{col 58} 4.042586{col 70} 5.463624
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo DRP2: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if include1 == 1 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -10033.87}  
Iteration 1:{space 3}log pseudolikelihood = {res:-10033.868}  
Iteration 2:{space 3}log pseudolikelihood = {res:-10033.868}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     4,556
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        45
{txt}{col 63}avg{col 67}={col 69}{res}     207.1
{txt}{col 63}max{col 67}={col 69}{res}       538
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   201.79
{txt}Log pseudolikelihood = {res}-10033.868{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0703184{col 27}{space 2} .0118501{col 38}{space 1}   -5.93{col 47}{space 3}0.000{col 55}{space 4}-.0935442{col 68}{space 3}-.0470926
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4539563{col 27}{space 2} .1231862{col 38}{space 1}   -3.69{col 47}{space 3}0.000{col 55}{space 4}-.6953969{col 68}{space 3}-.2125158
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1885028{col 27}{space 2} .0884889{col 38}{space 1}    2.13{col 47}{space 3}0.033{col 55}{space 4} .0150677{col 68}{space 3} .3619379
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0233778{col 27}{space 2} .0162672{col 38}{space 1}   -1.44{col 47}{space 3}0.151{col 55}{space 4} -.055261{col 68}{space 3} .0085054
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0304253{col 27}{space 2} .0185721{col 38}{space 1}    1.64{col 47}{space 3}0.101{col 55}{space 4}-.0059754{col 68}{space 3}  .066826
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0261703{col 27}{space 2}  .081987{col 38}{space 1}    0.32{col 47}{space 3}0.750{col 55}{space 4}-.1345213{col 68}{space 3} .1868619
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} .0858502{col 27}{space 2} .1885354{col 38}{space 1}    0.46{col 47}{space 3}0.649{col 55}{space 4}-.2836723{col 68}{space 3} .4553728
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1906279{col 27}{space 2} .0433015{col 38}{space 1}    4.40{col 47}{space 3}0.000{col 55}{space 4} .1057584{col 68}{space 3} .2754974
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0876641{col 27}{space 2} .0788433{col 38}{space 1}   -1.11{col 47}{space 3}0.266{col 55}{space 4} -.242194{col 68}{space 3} .0668659
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.9886606{col 27}{space 2} .3030713{col 38}{space 1}   -3.26{col 47}{space 3}0.001{col 55}{space 4}-1.582669{col 68}{space 3}-.3946518
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0379274{col 27}{space 2} .0286166{col 38}{space 1}   -1.33{col 47}{space 3}0.185{col 55}{space 4}-.0940149{col 68}{space 3} .0181602
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0123365{col 27}{space 2} .0356932{col 38}{space 1}   -0.35{col 47}{space 3}0.730{col 55}{space 4}-.0822938{col 68}{space 3} .0576209
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0626746{col 27}{space 2} .0322048{col 38}{space 1}    1.95{col 47}{space 3}0.052{col 55}{space 4}-.0004456{col 68}{space 3} .1257948
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  11.1485{col 27}{space 2} 2.580313{col 38}{space 1}    4.32{col 47}{space 3}0.000{col 55}{space 4}  6.09118{col 68}{space 3} 16.20582
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0882641{col 44} .0690646{col 58} .0190431{col 70} .4091001
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.012213{col 44} .3682617{col 58} .4961228{col 70} 2.065165
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.698891{col 44} .3609654{col 58} 4.042096{col 70} 5.462407
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 28
. // Only include respondents who rate civil rights protection (V136) as strictly higher
. // than all one of the other "strange" items (V131, V132, V134, V135, V137, V138)
. gen include2 = 0
{txt}
{com}. replace include2 = 1 if V136 > V131 & V136 > V132 & V136 > V134 ///
>         & V136 > V135 & V136 > V137 & V136 > V138
{txt}(3,178 real changes made)

{com}. eststo DRP3: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if include2 == 1 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6630.2363}  
Iteration 1:{space 3}log pseudolikelihood = {res:-6630.2292}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6630.2292}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     3,014
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        26
{txt}{col 63}avg{col 67}={col 69}{res}     137.0
{txt}{col 63}max{col 67}={col 69}{res}       415
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   141.43
{txt}Log pseudolikelihood = {res}-6630.2292{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1183991{col 27}{space 2} .0304226{col 38}{space 1}   -3.89{col 47}{space 3}0.000{col 55}{space 4}-.1780262{col 68}{space 3}-.0587719
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4589144{col 27}{space 2} .1343751{col 38}{space 1}   -3.42{col 47}{space 3}0.001{col 55}{space 4}-.7222847{col 68}{space 3}-.1955441
{txt}{space 7}female {c |}{col 15}{res}{space 2} .2033763{col 27}{space 2} .1120091{col 38}{space 1}    1.82{col 47}{space 3}0.069{col 55}{space 4}-.0161576{col 68}{space 3} .4229101
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0198987{col 27}{space 2} .0125826{col 38}{space 1}   -1.58{col 47}{space 3}0.114{col 55}{space 4}-.0445601{col 68}{space 3} .0047628
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0299161{col 27}{space 2} .0149097{col 38}{space 1}    2.01{col 47}{space 3}0.045{col 55}{space 4} .0006935{col 68}{space 3} .0591386
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.1655738{col 27}{space 2} .1332753{col 38}{space 1}   -1.24{col 47}{space 3}0.214{col 55}{space 4}-.4267885{col 68}{space 3}  .095641
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} .0502085{col 27}{space 2}  .162019{col 38}{space 1}    0.31{col 47}{space 3}0.757{col 55}{space 4} -.267343{col 68}{space 3}   .36776
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1777617{col 27}{space 2}   .05233{col 38}{space 1}    3.40{col 47}{space 3}0.001{col 55}{space 4} .0751967{col 68}{space 3} .2803266
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0807053{col 27}{space 2} .0945226{col 38}{space 1}   -0.85{col 47}{space 3}0.393{col 55}{space 4}-.2659663{col 68}{space 3} .1045556
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6542818{col 27}{space 2} .2871937{col 38}{space 1}   -2.28{col 47}{space 3}0.023{col 55}{space 4}-1.217171{col 68}{space 3}-.0913925
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0087391{col 27}{space 2} .0257629{col 38}{space 1}   -0.34{col 47}{space 3}0.734{col 55}{space 4}-.0592335{col 68}{space 3} .0417554
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2} .0279446{col 27}{space 2} .0418728{col 38}{space 1}    0.67{col 47}{space 3}0.505{col 55}{space 4}-.0541247{col 68}{space 3} .1100138
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0451462{col 27}{space 2} .0375591{col 38}{space 1}    1.20{col 47}{space 3}0.229{col 55}{space 4}-.0284682{col 68}{space 3} .1187606
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.513875{col 27}{space 2} 2.153906{col 38}{space 1}    3.49{col 47}{space 3}0.000{col 55}{space 4} 3.292296{col 68}{space 3} 11.73545
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .0972348{col 44} .0915398{col 58}  .015363{col 70} .6154162
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.047212{col 44} .2597384{col 58} .6440395{col 70} 1.702773
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.645071{col 44} .2985583{col 58} 4.095265{col 70}  5.26869
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo DRP4: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if include2 == 1 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-6631.3877}  
Iteration 1:{space 3}log pseudolikelihood = {res:-6631.3839}  
Iteration 2:{space 3}log pseudolikelihood = {res:-6631.3839}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     3,014
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        26
{txt}{col 63}avg{col 67}={col 69}{res}     137.0
{txt}{col 63}max{col 67}={col 69}{res}       415
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   157.49
{txt}Log pseudolikelihood = {res}-6631.3839{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2}-.0729802{col 27}{space 2}  .014101{col 38}{space 1}   -5.18{col 47}{space 3}0.000{col 55}{space 4}-.1006177{col 68}{space 3}-.0453427
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.4674349{col 27}{space 2} .1356725{col 38}{space 1}   -3.45{col 47}{space 3}0.001{col 55}{space 4}-.7333481{col 68}{space 3}-.2015218
{txt}{space 7}female {c |}{col 15}{res}{space 2} .2029839{col 27}{space 2} .1123125{col 38}{space 1}    1.81{col 47}{space 3}0.071{col 55}{space 4}-.0171446{col 68}{space 3} .4231123
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0201444{col 27}{space 2} .0125864{col 38}{space 1}   -1.60{col 47}{space 3}0.109{col 55}{space 4}-.0448133{col 68}{space 3} .0045245
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0301915{col 27}{space 2} .0149037{col 38}{space 1}    2.03{col 47}{space 3}0.043{col 55}{space 4} .0009808{col 68}{space 3} .0594021
{txt}{space 6}married {c |}{col 15}{res}{space 2}-.1681889{col 27}{space 2} .1342315{col 38}{space 1}   -1.25{col 47}{space 3}0.210{col 55}{space 4}-.4312778{col 68}{space 3}    .0949
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}  .047738{col 27}{space 2} .1617678{col 38}{space 1}    0.30{col 47}{space 3}0.768{col 55}{space 4}-.2693211{col 68}{space 3}  .364797
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1788727{col 27}{space 2} .0520349{col 38}{space 1}    3.44{col 47}{space 3}0.001{col 55}{space 4} .0768861{col 68}{space 3} .2808592
{txt}{space 1}social_class {c |}{col 15}{res}{space 2}-.0807769{col 27}{space 2} .0943612{col 38}{space 1}   -0.86{col 47}{space 3}0.392{col 55}{space 4}-.2657215{col 68}{space 3} .1041677
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.8174548{col 27}{space 2} .3273445{col 38}{space 1}   -2.50{col 47}{space 3}0.013{col 55}{space 4}-1.459038{col 68}{space 3}-.1758714
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0423312{col 27}{space 2} .0301399{col 38}{space 1}   -1.40{col 47}{space 3}0.160{col 55}{space 4}-.1014043{col 68}{space 3}  .016742
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0321685{col 27}{space 2} .0400762{col 38}{space 1}   -0.80{col 47}{space 3}0.422{col 55}{space 4}-.1107164{col 68}{space 3} .0463794
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0761493{col 27}{space 2} .0372062{col 38}{space 1}    2.05{col 47}{space 3}0.041{col 55}{space 4} .0032264{col 68}{space 3} .1490722
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.599944{col 27}{space 2} 2.697594{col 38}{space 1}    3.56{col 47}{space 3}0.000{col 55}{space 4} 4.312758{col 68}{space 3} 14.88713
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} .1156181{col 44} .1020962{col 58} .0204821{col 70} .6526445
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.168661{col 44} .4021625{col 58} .5953478{col 70} 2.294067
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 4.643378{col 44} .2983303{col 58} 4.093979{col 70} 5.266505
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}. 
. ** Reference in "rob-check_summary.xlsx": rob_check == 29
. // Only include respondents who rate the mean of free elections and civil rights 
. // protection as strictly higher than the mean of all other "strange" items
. gen mean_nonstrange = .
{txt}(31,742 missing values generated)

{com}. replace mean_nonstrange = (V133 + V136) / 2 ///
>         if V133 > 0 & V136 > 0
{txt}(30,383 real changes made)

{com}. gen mean_strange = .
{txt}(31,742 missing values generated)

{com}. replace mean_strange = (V131 + V132 + V134 + V135 + V137 + V138) / 6 ///
>         if V131 > 0 & V132 > 0 & V134 > 0 & V135 > 0 & V137 > 0 & V138 > 0
{txt}(27,339 real changes made)

{com}. gen include3 = 0
{txt}
{com}. replace include3 = 1 if mean_strange > mean_nonstrange
{txt}(8,662 real changes made)

{com}. eststo DRP5: mixed diff_dem_vdem new_fotp university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if include3 == 1 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-18932.056}  
Iteration 1:{space 3}log pseudolikelihood = {res:-18931.507}  
Iteration 2:{space 3}log pseudolikelihood = {res:-18931.456}  
Iteration 3:{space 3}log pseudolikelihood = {res:-18931.456}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     8,293
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       125
{txt}{col 63}avg{col 67}={col 69}{res}     377.0
{txt}{col 63}max{col 67}={col 69}{res}     1,523
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   329.83
{txt}Log pseudolikelihood = {res}-18931.456{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}new_fotp {c |}{col 15}{res}{space 2}-.1094688{col 27}{space 2} .0222538{col 38}{space 1}   -4.92{col 47}{space 3}0.000{col 55}{space 4}-.1530854{col 68}{space 3}-.0658522
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3336816{col 27}{space 2} .0803811{col 38}{space 1}   -4.15{col 47}{space 3}0.000{col 55}{space 4}-.4912257{col 68}{space 3}-.1761375
{txt}{space 7}female {c |}{col 15}{res}{space 2}  .135386{col 27}{space 2} .0627458{col 38}{space 1}    2.16{col 47}{space 3}0.031{col 55}{space 4} .0124065{col 68}{space 3} .2583655
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0165651{col 27}{space 2}  .008335{col 38}{space 1}   -1.99{col 47}{space 3}0.047{col 55}{space 4}-.0329015{col 68}{space 3}-.0002288
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0205515{col 27}{space 2} .0094683{col 38}{space 1}    2.17{col 47}{space 3}0.030{col 55}{space 4} .0019941{col 68}{space 3}  .039109
{txt}{space 6}married {c |}{col 15}{res}{space 2} .1001635{col 27}{space 2} .0716978{col 38}{space 1}    1.40{col 47}{space 3}0.162{col 55}{space 4}-.0403617{col 68}{space 3} .2406886
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1080404{col 27}{space 2} .1614326{col 38}{space 1}   -0.67{col 47}{space 3}0.503{col 55}{space 4}-.4244425{col 68}{space 3} .2083617
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1743431{col 27}{space 2}    .0365{col 38}{space 1}    4.78{col 47}{space 3}0.000{col 55}{space 4} .1028043{col 68}{space 3} .2458818
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0099804{col 27}{space 2} .0710244{col 38}{space 1}    0.14{col 47}{space 3}0.888{col 55}{space 4}-.1292249{col 68}{space 3} .1491856
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.4564912{col 27}{space 2}  .255628{col 38}{space 1}   -1.79{col 47}{space 3}0.074{col 55}{space 4}-.9575129{col 68}{space 3} .0445305
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0021676{col 27}{space 2} .0244496{col 38}{space 1}   -0.09{col 47}{space 3}0.929{col 55}{space 4} -.050088{col 68}{space 3} .0457527
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}  .013341{col 27}{space 2} .0440469{col 38}{space 1}    0.30{col 47}{space 3}0.762{col 55}{space 4}-.0729893{col 68}{space 3} .0996714
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0431466{col 27}{space 2} .0338563{col 38}{space 1}    1.27{col 47}{space 3}0.203{col 55}{space 4}-.0232106{col 68}{space 3} .1095038
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.238836{col 27}{space 2} 2.204139{col 38}{space 1}    2.83{col 47}{space 3}0.005{col 55}{space 4} 1.918803{col 68}{space 3} 10.55887
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} 1.94e-14{col 44} 4.25e-09{col 58}        0{col 70}        .
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.007121{col 44} .2322699{col 58} .6408717{col 70} 1.582677
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.567448{col 44}  .570005{col 58} 4.555213{col 70} 6.804617
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo DRP6: mixed diff_dem_vdem new_msf university ///
>         female age age_sq married unemployed income social_class ///
>         ln_gdp growth_one_yr new_rol new_gov ///
>         if include3 == 1 || ///
>         code: university, vce(cluster code) mle
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: {res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-18933.135}  
Iteration 1:{space 3}log pseudolikelihood = {res:-18932.585}  
Iteration 2:{space 3}log pseudolikelihood = {res:-18932.538}  
Iteration 3:{space 3}log pseudolikelihood = {res:-18932.538}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 49}Number of obs{col 67}={col 69}{res}     8,293
{txt}Group variable: {res}code{col 49}{txt}Number of groups{col 67}={col 69}{res}        22
{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}       125
{txt}{col 63}avg{col 67}={col 69}{res}     377.0
{txt}{col 63}max{col 67}={col 69}{res}     1,523
{col 49}{txt}Wald chi2({res}13{txt}){col 67}={col 70}{res}   225.25
{txt}Log pseudolikelihood = {res}-18932.538{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:22} clusters in {res:code})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}diff_dem_vdem{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}new_msf {c |}{col 15}{res}{space 2} -.066773{col 27}{space 2} .0123934{col 38}{space 1}   -5.39{col 47}{space 3}0.000{col 55}{space 4}-.0910635{col 68}{space 3}-.0424824
{txt}{space 3}university {c |}{col 15}{res}{space 2}-.3331305{col 27}{space 2} .0805705{col 38}{space 1}   -4.13{col 47}{space 3}0.000{col 55}{space 4}-.4910458{col 68}{space 3}-.1752151
{txt}{space 7}female {c |}{col 15}{res}{space 2} .1350984{col 27}{space 2}  .062713{col 38}{space 1}    2.15{col 47}{space 3}0.031{col 55}{space 4} .0121832{col 68}{space 3} .2580136
{txt}{space 10}age {c |}{col 15}{res}{space 2} -.016608{col 27}{space 2} .0083573{col 38}{space 1}   -1.99{col 47}{space 3}0.047{col 55}{space 4} -.032988{col 68}{space 3}-.0002281
{txt}{space 7}age_sq {c |}{col 15}{res}{space 2} .0205824{col 27}{space 2}  .009484{col 38}{space 1}    2.17{col 47}{space 3}0.030{col 55}{space 4} .0019941{col 68}{space 3} .0391706
{txt}{space 6}married {c |}{col 15}{res}{space 2} .0993321{col 27}{space 2} .0720768{col 38}{space 1}    1.38{col 47}{space 3}0.168{col 55}{space 4}-.0419358{col 68}{space 3}    .2406
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.1100212{col 27}{space 2}  .161763{col 38}{space 1}   -0.68{col 47}{space 3}0.496{col 55}{space 4}-.4270708{col 68}{space 3} .2070283
{txt}{space 7}income {c |}{col 15}{res}{space 2} .1743825{col 27}{space 2} .0364509{col 38}{space 1}    4.78{col 47}{space 3}0.000{col 55}{space 4} .1029401{col 68}{space 3}  .245825
{txt}{space 1}social_class {c |}{col 15}{res}{space 2} .0097697{col 27}{space 2} .0710786{col 38}{space 1}    0.14{col 47}{space 3}0.891{col 55}{space 4}-.1295418{col 68}{space 3} .1490813
{txt}{space 7}ln_gdp {c |}{col 15}{res}{space 2}-.6137235{col 27}{space 2} .3070003{col 38}{space 1}   -2.00{col 47}{space 3}0.046{col 55}{space 4}-1.215433{col 68}{space 3} -.012014
{txt}growth_one_yr {c |}{col 15}{res}{space 2}-.0331004{col 27}{space 2} .0350058{col 38}{space 1}   -0.95{col 47}{space 3}0.344{col 55}{space 4}-.1017104{col 68}{space 3} .0355097
{txt}{space 6}new_rol {c |}{col 15}{res}{space 2}-.0416667{col 27}{space 2} .0455385{col 38}{space 1}   -0.91{col 47}{space 3}0.360{col 55}{space 4}-.1309204{col 68}{space 3} .0475871
{txt}{space 6}new_gov {c |}{col 15}{res}{space 2} .0717094{col 27}{space 2} .0358277{col 38}{space 1}    2.00{col 47}{space 3}0.045{col 55}{space 4} .0014884{col 68}{space 3} .1419304
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 8.178382{col 27}{space 2} 2.797184{col 38}{space 1}    2.92{col 47}{space 3}0.003{col 55}{space 4} 2.696001{col 68}{space 3} 13.66076
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}code{txt}: Independent{col 30}{c |}
{space 13}var(university) {c |}{res}{col 33} 7.61e-15{col 44} 1.81e-09{col 58}        0{col 70}        .
{txt}{space 18}var(_cons) {c |}{res}{col 33} 1.113791{col 44} .3663383{col 58}  .584567{col 70} 2.122137
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 5.567441{col 44} .5700245{col 58} 4.555175{col 70} 6.804657
{txt}{hline 29}{c BT}{hline 48}

{com}. eststo clear
{txt}
{com}.         
. *** Figure S18 ***
. * ssc install binscatter
. // Panel A
. binscatter diff_dem_vdem new_fotp, ///
>         controls(university female age age_sq married unemployed ///
>         income social_class ln_gdp growth_one_yr new_rol new_gov) ///
>         xtitle(Freedom of the Press (Residualized)) ytitle(Degree of Overestimation (Residualized)) ///
>         xscale(range(0 60)) yscale(range(-1 4)) ///
>         ylabel(#6) xlabel(#6) ysize(5) xsize(5)
{res}{txt}
{com}. graph export "~/Desktop/CPS_replication/figures/Figure S18a.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Desktop/CPS_replication/figures/Figure S18a.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. // Panel B
. binscatter diff_dem_vdem new_msf, ///
>         controls(university female age age_sq married unemployed ///
>         income social_class ln_gdp growth_one_yr new_rol new_gov) ///
>         xtitle(Media System Freedom (Residualized)) ytitle(Degree of Overestimation (Residualized)) ///
>         xscale(range(0 80)) yscale(range(-1 4)) ///
>         ylabel(#6) xlabel(#6) ysize(5) xsize(5)
{res}{txt}
{com}. graph export "~/Desktop/CPS_replication/figures/Figure S18b.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Desktop/CPS_replication/figures/Figure S18b.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. *** Page S11 of Supplemental Appendix ***
. /* Electoral fraud, Internet censorship, and Internet consumption */
. // Drop countries that are not asked a full set of questions about electoral fairness
. drop if code == 51 | code == 112 | code == 156 | code == 400 | code == 504 | code == 643
{txt}(8,930 observations deleted)

{com}. 
. // Code perceptions of the electoral process using V228A to V228I in WVS
. gen fairness1 = .
{txt}(22,812 missing values generated)

{com}. replace fairness1 = 0 if V228A == 4
{txt}(2,625 real changes made)

{com}. replace fairness1 = 1 if V228A == 3
{txt}(5,670 real changes made)

{com}. replace fairness1 = 2 if V228A == 2
{txt}(6,403 real changes made)

{com}. replace fairness1 = 3 if V228A == 1
{txt}(5,102 real changes made)

{com}. 
. gen fairness2 = .
{txt}(22,812 missing values generated)

{com}. replace fairness2 = 0 if V228B == 1
{txt}(2,200 real changes made)

{com}. replace fairness2 = 1 if V228B == 2
{txt}(5,448 real changes made)

{com}. replace fairness2 = 2 if V228B == 3
{txt}(6,061 real changes made)

{com}. replace fairness2 = 3 if V228B == 4
{txt}(4,697 real changes made)

{com}. 
. gen fairness3 = .
{txt}(22,812 missing values generated)

{com}. replace fairness3 = 0 if V228C == 1
{txt}(4,424 real changes made)

{com}. replace fairness3 = 1 if V228C == 2
{txt}(6,914 real changes made)

{com}. replace fairness3 = 2 if V228C == 3
{txt}(4,892 real changes made)

{com}. replace fairness3 = 3 if V228C == 4
{txt}(2,904 real changes made)

{com}. 
. gen fairness4 = .
{txt}(22,812 missing values generated)

{com}. replace fairness4 = 0 if V228D == 1
{txt}(3,626 real changes made)

{com}. replace fairness4 = 1 if V228D == 2
{txt}(5,733 real changes made)

{com}. replace fairness4 = 2 if V228D == 3
{txt}(4,835 real changes made)

{com}. replace fairness4 = 3 if V228D == 4
{txt}(4,336 real changes made)

{com}. 
. gen fairness5 = .
{txt}(22,812 missing values generated)

{com}. replace fairness5 = 0 if V228E == 4
{txt}(2,391 real changes made)

{com}. replace fairness5 = 1 if V228E == 3
{txt}(5,902 real changes made)

{com}. replace fairness5 = 2 if V228E == 2
{txt}(7,474 real changes made)

{com}. replace fairness5 = 3 if V228E == 1
{txt}(3,285 real changes made)

{com}. 
. gen fairness6 = .
{txt}(22,812 missing values generated)

{com}. replace fairness6 = 0 if V228F == 4
{txt}(3,022 real changes made)

{com}. replace fairness6 = 1 if V228F == 3
{txt}(6,414 real changes made)

{com}. replace fairness6 = 2 if V228F == 2
{txt}(5,968 real changes made)

{com}. replace fairness6 = 3 if V228F == 1
{txt}(3,434 real changes made)

{com}. 
. gen fairness7 = .
{txt}(22,812 missing values generated)

{com}. replace fairness7 = 0 if V228G == 1
{txt}(4,305 real changes made)

{com}. replace fairness7 = 1 if V228G == 2
{txt}(5,188 real changes made)

{com}. replace fairness7 = 2 if V228G == 3
{txt}(4,487 real changes made)

{com}. replace fairness7 = 3 if V228G == 4
{txt}(4,016 real changes made)

{com}. 
. gen fairness8 = .
{txt}(22,812 missing values generated)

{com}. replace fairness8 = 0 if V228H == 1
{txt}(1,958 real changes made)

{com}. replace fairness8 = 1 if V228H == 2
{txt}(3,817 real changes made)

{com}. replace fairness8 = 2 if V228H == 3
{txt}(4,790 real changes made)

{com}. replace fairness8 = 3 if V228H == 4
{txt}(7,558 real changes made)

{com}. 
. gen fairness9 = .
{txt}(22,812 missing values generated)

{com}. replace fairness9 = 0 if V228I == 4
{txt}(2,555 real changes made)

{com}. replace fairness9 = 1 if V228I == 3
{txt}(4,549 real changes made)

{com}. replace fairness9 = 2 if V228I == 2
{txt}(6,820 real changes made)

{com}. replace fairness9 = 3 if V228I == 1
{txt}(4,854 real changes made)

{com}. 
. // Predict the missing values using multiple imputation
. mi set wide
{txt}
{com}. mi register imputed fairness1-fairness9
{res}{txt}
{com}. set seed 1234567
{txt}
{com}. mi impute mvn fairness1-fairness9, add(1)
{res}
{txt}Performing EM optimization:
{txt}{p 0 6}note: 1776 {txt}observations {txt}omitted from EM estimation because of all {txt}imputation variables missing.{p_end}
{txt}  observed log likelihood = {res}-63723.392{txt} at iteration 10
{res}
{txt}Performing MCMC data augmentation ... 
{res}{txt}
Multivariate imputation{txt}{col 45}{ralign 12:Imputations }= {res}       1
{txt}Multivariate normal regression{txt}{col 45}{ralign 12:added }= {res}       1
{txt}Imputed: {it:m}=1{txt}{col 45}{ralign 12:updated }= {res}       0

{txt}Prior: uniform{txt}{col 45}{ralign 12:Iterations }= {res}     100
{txt}{col 45}{ralign 12:burn-in }= {res}     100
{txt}{col 45}{ralign 12:between }= {res}     100

{txt}{hline 19}{c TT}{hline 35}{hline 11}
{txt}{col 20}{c |}{center 46:  Observations per {it:m}}
{txt}{col 20}{c LT}{hline 35}{c TT}{hline 10}
{txt}{col 11}Variable {c |}{ralign 12:Complete }{ralign 13:Incomplete }{ralign 10:Imputed }{c |}{ralign 10:Total}
{hline 19}{c +}{hline 35}{c +}{hline 10}
{txt}{ralign 19:fairness1 }{c |}{res}      19800         3012      3012 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness2 }{c |}{res}      18406         4406      4406 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness3 }{c |}{res}      19134         3678      3678 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness4 }{c |}{res}      18530         4282      4282 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness5 }{c |}{res}      19052         3760      3760 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness6 }{c |}{res}      18838         3974      3974 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness7 }{c |}{res}      17996         4816      4816 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness8 }{c |}{res}      18123         4689      4689 {txt}{c |}{res}     22812
{txt}{ralign 19:fairness9 }{c |}{res}      18778         4034      4034 {txt}{c |}{res}     22812
{txt}{hline 19}{c BT}{hline 35}{c BT}{hline 10}
{p 0 1 1 66}(Complete + Incomplete = Total; Imputed is the minimum across {it:m}
 of the number of filled-in observations.){p_end}
{res}{txt}
{com}. 
. // Create an electoral fairness index using principal component analysis
. pca _1_fairness1-_1_fairness9

{txt}Principal components/correlation{col 50}Number of obs    = {res}    22,812
{col 50}{txt}Number of comp.  = {res}         9
{col 50}{txt}Trace            {col 67}=  {res}        9
{col 5}{txt}Rotation: (unrotated = principal){col 50}Rho              = {res}    1.0000

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}   Component {c |}   Eigenvalue   Difference         Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 12:Comp1} {c |}{res}       3.0686      1.23089             0.3410       0.3410
{txt}{col 5}{ralign 12:Comp2} {c |}{res}      1.83771       1.0368             0.2042       0.5451
{txt}{col 5}{ralign 12:Comp3} {c |}{res}      .800913     .0762096             0.0890       0.6341
{txt}{col 5}{ralign 12:Comp4} {c |}{res}      .724703     .0886549             0.0805       0.7147
{txt}{col 5}{ralign 12:Comp5} {c |}{res}      .636048     .0484728             0.0707       0.7853
{txt}{col 5}{ralign 12:Comp6} {c |}{res}      .587575     .0347312             0.0653       0.8506
{txt}{col 5}{ralign 12:Comp7} {c |}{res}      .552844      .102604             0.0614       0.9120
{txt}{col 5}{ralign 12:Comp8} {c |}{res}       .45024       .10888             0.0500       0.9621
{txt}{col 5}{ralign 12:Comp9} {c |}{res}       .34136            .             0.0379       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}

Principal components (eigenvectors) 

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}{space 1}{ralign 8:Comp2}{space 1}{space 1}{ralign 8:Comp3}{space 1}{space 1}{ralign 8:Comp4}{space 1}{space 1}{ralign 8:Comp5}{space 1}{space 1}{ralign 8:Comp6}{space 1}{space 1}{ralign 8:Comp7}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:_1_fairness1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3457}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3336}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3303}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1170}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1272}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4456}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3172}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3044}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2588}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6089}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3847}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1875}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.5083}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1247}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness3}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3182}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2637}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4229}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4878}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4716}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1770}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3849}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness4}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4290}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2400}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2219}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1539}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0273}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0982}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4138}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness5}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1772}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4729}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3742}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3057}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6025}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3394}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0073}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness6}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3215}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4331}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0694}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0761}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0266}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3419}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0159}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness7}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4314}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2169}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2459}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1275}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1344}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1360}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4106}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness8}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3632}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2390}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4572}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2918}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3142}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5886}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness9}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2233}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4375}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1717}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5023}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.5084}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3908}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2215}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 13}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp8}{space 1}{space 1}{ralign 8:Comp9}{space 1}{c |}{space 1}{ralign 11:Unexplained}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 13}
{space 4}{space 0}{ralign 12:_1_fairness1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5742}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0374}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1110}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0111}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness3}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0693}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0757}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness4}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0252}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.7094}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness5}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1584}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0897}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness6}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.7582}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0760}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness7}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1059}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.6883}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness8}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1591}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0385}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness9}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1270}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0227}}}{space 1}{c |}{space 1}{center 11:{res:{sf:          0}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 13}

{com}. predict per_fairness_pca, score
{txt}(8 components skipped)

Scoring coefficients 
{col 5}sum of squares(column-loading) = 1

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp1}{space 1}{space 1}{ralign 8:Comp2}{space 1}{space 1}{ralign 8:Comp3}{space 1}{space 1}{ralign 8:Comp4}{space 1}{space 1}{ralign 8:Comp5}{space 1}{space 1}{ralign 8:Comp6}{space 1}{space 1}{ralign 8:Comp7}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:_1_fairness1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3457}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3336}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3303}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1170}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1272}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4456}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3172}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3044}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2588}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6089}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3847}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1875}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.5083}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1247}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness3}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3182}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2637}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4229}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4878}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4716}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1770}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3849}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness4}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4290}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2400}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2219}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1539}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0273}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0982}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4138}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness5}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1772}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4729}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3742}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3057}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6025}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3394}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0073}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness6}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3215}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4331}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0694}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0761}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0266}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3419}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0159}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness7}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4314}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2169}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2459}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1275}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1344}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1360}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4106}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness8}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3632}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2118}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2390}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4572}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2918}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3142}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5886}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness9}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2233}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4375}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1717}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5023}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.5084}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3908}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2215}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Comp8}{space 1}{space 1}{ralign 8:Comp9}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:_1_fairness1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5742}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0374}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1110}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0111}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness3}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0693}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0757}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness4}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0252}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.7094}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness5}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1584}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0897}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness6}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.7582}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0760}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness7}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1059}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.6883}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness8}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1591}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0385}}}{space 1}
{space 4}{space 0}{ralign 12:_1_fairness9}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1270}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0227}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}

{com}. 
. // Regress the electoral fairness index on Internet consumption and university
. // education, along with country-fixed effects (which remove unobserved
. // between-group heterogeneity)
. // Cluster standard errors are not needed here because each within-group 
. // observation can be considered as an i.i.d. draw from their larger group
. reg per_fairness_pca internet university ///
>         female age age_sq married unemployed income social_class ///
>         i.code, robust

{txt}Linear regression                               Number of obs     = {res}    21,863
                                                {txt}F(24, 21838)      =  {res}   475.85
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2167
                                                {txt}Root MSE          =    {res} 1.5543

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}per_fairne~a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}internet {c |}{col 14}{res}{space 2}-.0363323{col 26}{space 2} .0076483{col 37}{space 1}   -4.75{col 46}{space 3}0.000{col 54}{space 4}-.0513234{col 67}{space 3}-.0213411
{txt}{space 2}university {c |}{col 14}{res}{space 2}-.0836608{col 26}{space 2} .0314028{col 37}{space 1}   -2.66{col 46}{space 3}0.008{col 54}{space 4}-.1452126{col 67}{space 3} -.022109
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0272995{col 26}{space 2} .0212807{col 37}{space 1}    1.28{col 46}{space 3}0.200{col 54}{space 4}-.0144123{col 67}{space 3} .0690113
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0039202{col 26}{space 2}  .003841{col 37}{space 1}    1.02{col 46}{space 3}0.307{col 54}{space 4}-.0036085{col 67}{space 3}  .011449
{txt}{space 6}age_sq {c |}{col 14}{res}{space 2}-.0005478{col 26}{space 2} .0042118{col 37}{space 1}   -0.13{col 46}{space 3}0.897{col 54}{space 4}-.0088032{col 67}{space 3} .0077077
{txt}{space 5}married {c |}{col 14}{res}{space 2}  .021342{col 26}{space 2} .0255205{col 37}{space 1}    0.84{col 46}{space 3}0.403{col 54}{space 4}-.0286801{col 67}{space 3} .0713642
{txt}{space 2}unemployed {c |}{col 14}{res}{space 2}-.1359683{col 26}{space 2} .0363472{col 37}{space 1}   -3.74{col 46}{space 3}0.000{col 54}{space 4}-.2072114{col 67}{space 3}-.0647251
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0506438{col 26}{space 2} .0064071{col 37}{space 1}    7.90{col 46}{space 3}0.000{col 54}{space 4} .0380855{col 67}{space 3} .0632022
{txt}social_class {c |}{col 14}{res}{space 2} -.000488{col 26}{space 2} .0131692{col 37}{space 1}   -0.04{col 46}{space 3}0.970{col 54}{space 4}-.0263006{col 67}{space 3} .0253245
{txt}{space 12} {c |}
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